View Jupyter notebook on the GitHub.
Deep learning examples#
This notebooks contains examples with neural network models.
Table of contents
Loading dataset
Architecture
Testing models
Baseline
DeepAR
DeepARNative
TFT
TFTNative
RNN
MLP
Deep State Model
N-BEATS Model
PatchTS Model
[1]:
!pip install "etna[torch]" -q
[2]:
import warnings
warnings.filterwarnings("ignore")
[3]:
import random
import numpy as np
import pandas as pd
import torch
from etna.analysis import plot_backtest
from etna.datasets.tsdataset import TSDataset
from etna.metrics import MAE
from etna.metrics import MAPE
from etna.metrics import SMAPE
from etna.models import SeasonalMovingAverageModel
from etna.pipeline import Pipeline
from etna.transforms import DateFlagsTransform
from etna.transforms import LabelEncoderTransform
from etna.transforms import LagTransform
from etna.transforms import LinearTrendTransform
from etna.transforms import SegmentEncoderTransform
from etna.transforms import StandardScalerTransform
[4]:
def set_seed(seed: int = 42):
"""Set random seed for reproducibility."""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
1. Loading dataset#
We are going to take some toy dataset. Let’s load and look at it.
[5]:
df = pd.read_csv("data/example_dataset.csv")
df.head()
[5]:
| timestamp | segment | target | |
|---|---|---|---|
| 0 | 2019-01-01 | segment_a | 170 |
| 1 | 2019-01-02 | segment_a | 243 |
| 2 | 2019-01-03 | segment_a | 267 |
| 3 | 2019-01-04 | segment_a | 287 |
| 4 | 2019-01-05 | segment_a | 279 |
Our library works with the special data structure TSDataset. Let’s create it as it was done in “Get started” notebook.
[6]:
ts = TSDataset(df, freq="D")
ts.head(5)
[6]:
| segment | segment_a | segment_b | segment_c | segment_d |
|---|---|---|---|---|
| feature | target | target | target | target |
| timestamp | ||||
| 2019-01-01 | 170 | 102 | 92 | 238 |
| 2019-01-02 | 243 | 123 | 107 | 358 |
| 2019-01-03 | 267 | 130 | 103 | 366 |
| 2019-01-04 | 287 | 138 | 103 | 385 |
| 2019-01-05 | 279 | 137 | 104 | 384 |
2. Architecture#
Our library has two types of models:
Models from PyTorch Forecasting
Native models.
First, let’s describe the pytorch-forecasting models, because they require a special handling. There are two ways to use these models: default one and via using PytorchForecastingDatasetBuilder for using extra features.
To include extra features we use PytorchForecastingDatasetBuilder class.
Let’s look at it closer.
[7]:
from etna.models.nn.utils import PytorchForecastingDatasetBuilder
[8]:
?PytorchForecastingDatasetBuilder
Init signature:
PytorchForecastingDatasetBuilder(
max_encoder_length: int = 30,
min_encoder_length: Optional[int] = None,
min_prediction_idx: Optional[int] = None,
min_prediction_length: Optional[int] = None,
max_prediction_length: int = 1,
static_categoricals: Optional[List[str]] = None,
static_reals: Optional[List[str]] = None,
time_varying_known_categoricals: Optional[List[str]] = None,
time_varying_known_reals: Optional[List[str]] = None,
time_varying_unknown_categoricals: Optional[List[str]] = None,
time_varying_unknown_reals: Optional[List[str]] = None,
variable_groups: Optional[Dict[str, List[int]]] = None,
constant_fill_strategy: Optional[Dict[str, Union[str, float, int, bool]]] = None,
allow_missing_timesteps: bool = True,
lags: Optional[Dict[str, List[int]]] = None,
add_relative_time_idx: bool = True,
add_target_scales: bool = True,
add_encoder_length: Union[bool, str] = True,
target_normalizer: Union[pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.NaNLabelEncoder, pytorch_forecasting.data.encoders.EncoderNormalizer, str, List[Union[pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.NaNLabelEncoder, pytorch_forecasting.data.encoders.EncoderNormalizer]], Tuple[Union[pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.NaNLabelEncoder, pytorch_forecasting.data.encoders.EncoderNormalizer]]] = 'auto',
categorical_encoders: Optional[Dict[str, pytorch_forecasting.data.encoders.NaNLabelEncoder]] = None,
scalers: Optional[Dict[str, Union[sklearn.preprocessing._data.StandardScaler, sklearn.preprocessing._data.RobustScaler, pytorch_forecasting.data.encoders.TorchNormalizer, pytorch_forecasting.data.encoders.EncoderNormalizer]]] = None,
)
Docstring:
Builder for PytorchForecasting dataset.
Note
----
This class requires ``torch`` extension to be installed.
Read more about this at :ref:`installation page <installation>`.
Init docstring:
Init dataset builder.
Parameters here is used for initialization of :py:class:`pytorch_forecasting.data.timeseries.TimeSeriesDataSet` object.
File: /opt/conda/lib/python3.9/site-packages/etna/models/nn/utils.py
Type: type
Subclasses:
We can see a pretty scary signature, but don’t panic, we will look at the most important parameters.
time_varying_known_reals— known real values that change across the time (real regressors), now it it necessary to add “time_idx” variable to the list;time_varying_unknown_reals— our real value target, set it to["target"];max_prediction_length— our horizon for forecasting;max_encoder_length— length of past context to use;static_categoricals— static categorical values, for example, if we use multiple segments it can be some its characteristics including identifier: “segment”;time_varying_known_categoricals— known categorical values that change across the time (categorical regressors);target_normalizer— class for normalization targets across different segments.
Our library currently supports these pytorch-forecasting models:
As for the native neural network models, they are simpler to use, because they don’t require PytorchForecastingTransform. We will see how to use them on examples.
3. Testing models#
In this section we will test our models on example.
[9]:
HORIZON = 7
metrics = [SMAPE(), MAPE(), MAE()]
3.1 Baseline#
For comparison let’s train some simple model as a baseline.
[10]:
model_sma = SeasonalMovingAverageModel(window=5, seasonality=7)
linear_trend_transform = LinearTrendTransform(in_column="target")
pipeline_sma = Pipeline(model=model_sma, horizon=HORIZON, transforms=[linear_trend_transform])
[11]:
metrics_sma, forecast_sma, fold_info_sma = pipeline_sma.backtest(ts, metrics=metrics, n_folds=3, n_jobs=1)
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.4s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.4s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.2s
[12]:
metrics_sma
[12]:
| segment | SMAPE | MAPE | MAE | fold_number | |
|---|---|---|---|---|---|
| 0 | segment_a | 6.343943 | 6.124296 | 33.196532 | 0 |
| 0 | segment_a | 5.346946 | 5.192455 | 27.938101 | 1 |
| 0 | segment_a | 7.510347 | 7.189999 | 40.028565 | 2 |
| 1 | segment_b | 7.178822 | 6.920176 | 17.818102 | 0 |
| 1 | segment_b | 5.672504 | 5.554555 | 13.719200 | 1 |
| 1 | segment_b | 3.327846 | 3.359712 | 7.680919 | 2 |
| 2 | segment_c | 6.430429 | 6.200580 | 10.877718 | 0 |
| 2 | segment_c | 5.947090 | 5.727531 | 10.701336 | 1 |
| 2 | segment_c | 6.186545 | 5.943679 | 11.359563 | 2 |
| 3 | segment_d | 4.707899 | 4.644170 | 39.918646 | 0 |
| 3 | segment_d | 5.403426 | 5.600978 | 43.047332 | 1 |
| 3 | segment_d | 2.505279 | 2.543719 | 19.347565 | 2 |
[13]:
score = metrics_sma["SMAPE"].mean()
print(f"Average SMAPE for Seasonal MA: {score:.3f}")
Average SMAPE for Seasonal MA: 5.547
[14]:
plot_backtest(forecast_sma, ts, history_len=20)
3.2 DeepAR#
[15]:
from etna.models.nn import DeepARModel
Before training let’s fix seeds for reproducibility.
[16]:
set_seed()
Default way#
[17]:
model_deepar = DeepARModel(
encoder_length=HORIZON,
decoder_length=HORIZON,
trainer_params=dict(max_epochs=20, gradient_clip_val=0.1),
lr=0.01,
train_batch_size=16,
)
metrics = [SMAPE(), MAPE(), MAE()]
pipeline_deepar = Pipeline(model=model_deepar, horizon=HORIZON)
[18]:
metrics_deepar, forecast_deepar, fold_info_deepar = pipeline_deepar.backtest(ts, metrics=metrics, n_folds=3, n_jobs=1)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------------------------------
0 | loss | NormalDistributionLoss | 0
1 | logging_metrics | ModuleList | 0
2 | embeddings | MultiEmbedding | 0
3 | rnn | LSTM | 1.6 K
4 | distribution_projector | Linear | 22
------------------------------------------------------------------
1.6 K Trainable params
0 Non-trainable params
1.6 K Total params
0.006 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=20` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 54.2s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------------------------------
0 | loss | NormalDistributionLoss | 0
1 | logging_metrics | ModuleList | 0
2 | embeddings | MultiEmbedding | 0
3 | rnn | LSTM | 1.6 K
4 | distribution_projector | Linear | 22
------------------------------------------------------------------
1.6 K Trainable params
0 Non-trainable params
1.6 K Total params
0.006 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=20` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 1.8min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------------------------------
0 | loss | NormalDistributionLoss | 0
1 | logging_metrics | ModuleList | 0
2 | embeddings | MultiEmbedding | 0
3 | rnn | LSTM | 1.6 K
4 | distribution_projector | Linear | 22
------------------------------------------------------------------
1.6 K Trainable params
0 Non-trainable params
1.6 K Total params
0.006 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=20` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 2.7min
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 2.7min
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.4s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.4s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[19]:
metrics_deepar
[19]:
| segment | SMAPE | MAPE | MAE | fold_number | |
|---|---|---|---|---|---|
| 0 | segment_a | 11.991780 | 11.136381 | 60.220559 | 0 |
| 0 | segment_a | 3.738702 | 3.802561 | 19.429988 | 1 |
| 0 | segment_a | 9.372203 | 9.128006 | 47.781721 | 2 |
| 1 | segment_b | 8.085429 | 7.699512 | 20.147574 | 0 |
| 1 | segment_b | 4.951003 | 4.924274 | 11.986943 | 1 |
| 1 | segment_b | 5.498260 | 5.821215 | 12.443900 | 2 |
| 2 | segment_c | 5.561443 | 5.633259 | 9.442315 | 0 |
| 2 | segment_c | 7.060727 | 6.841533 | 12.387767 | 1 |
| 2 | segment_c | 5.387421 | 5.496503 | 9.709660 | 2 |
| 3 | segment_d | 6.425421 | 6.287131 | 55.566650 | 0 |
| 3 | segment_d | 3.536986 | 3.587421 | 27.874773 | 1 |
| 3 | segment_d | 6.445379 | 6.539991 | 50.958557 | 2 |
To summarize it we will take mean value of SMAPE metric because it is scale tolerant.
[20]:
score = metrics_deepar["SMAPE"].mean()
print(f"Average SMAPE for DeepAR: {score:.3f}")
Average SMAPE for DeepAR: 6.505
Dataset Builder: creating dataset for DeepAR with etxtra features.#
[21]:
from pytorch_forecasting.data import GroupNormalizer
num_lags = 10
transform_date = DateFlagsTransform(
day_number_in_week=True,
day_number_in_month=False,
day_number_in_year=False,
week_number_in_month=False,
week_number_in_year=False,
month_number_in_year=False,
season_number=False,
year_number=False,
is_weekend=False,
out_column="dateflag",
)
transform_lag = LagTransform(
in_column="target",
lags=[HORIZON + i for i in range(num_lags)],
out_column="target_lag",
)
lag_columns = [f"target_lag_{HORIZON+i}" for i in range(num_lags)]
dataset_builder_deepar = PytorchForecastingDatasetBuilder(
max_encoder_length=HORIZON,
max_prediction_length=HORIZON,
time_varying_known_reals=["time_idx"] + lag_columns,
time_varying_unknown_reals=["target"],
time_varying_known_categoricals=["dateflag_day_number_in_week"],
target_normalizer=GroupNormalizer(groups=["segment"]),
)
Now we are going to start backtest.
[22]:
set_seed()
model_deepar = DeepARModel(
dataset_builder=dataset_builder_deepar,
trainer_params=dict(max_epochs=20, gradient_clip_val=0.1),
lr=0.01,
train_batch_size=16,
)
pipeline_deepar = Pipeline(
model=model_deepar,
horizon=HORIZON,
transforms=[transform_lag, transform_date],
)
[23]:
metrics_deepar, forecast_deepar, fold_info_deepar = pipeline_deepar.backtest(ts, metrics=metrics, n_folds=3, n_jobs=1)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------------------------------
0 | loss | NormalDistributionLoss | 0
1 | logging_metrics | ModuleList | 0
2 | embeddings | MultiEmbedding | 35
3 | rnn | LSTM | 2.2 K
4 | distribution_projector | Linear | 22
------------------------------------------------------------------
2.3 K Trainable params
0 Non-trainable params
2.3 K Total params
0.009 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=20` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 52.0s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------------------------------
0 | loss | NormalDistributionLoss | 0
1 | logging_metrics | ModuleList | 0
2 | embeddings | MultiEmbedding | 35
3 | rnn | LSTM | 2.2 K
4 | distribution_projector | Linear | 22
------------------------------------------------------------------
2.3 K Trainable params
0 Non-trainable params
2.3 K Total params
0.009 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=20` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 1.8min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------------------------------
0 | loss | NormalDistributionLoss | 0
1 | logging_metrics | ModuleList | 0
2 | embeddings | MultiEmbedding | 35
3 | rnn | LSTM | 2.2 K
4 | distribution_projector | Linear | 22
------------------------------------------------------------------
2.3 K Trainable params
0 Non-trainable params
2.3 K Total params
0.009 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=20` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 2.7min
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 2.7min
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.4s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.7s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.7s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
Let’s compare results across different segments.
[24]:
metrics_deepar
[24]:
| segment | SMAPE | MAPE | MAE | fold_number | |
|---|---|---|---|---|---|
| 0 | segment_a | 6.805589 | 6.531080 | 35.181074 | 0 |
| 0 | segment_a | 3.220955 | 3.193370 | 16.990396 | 1 |
| 0 | segment_a | 6.250621 | 5.970211 | 33.195578 | 2 |
| 1 | segment_b | 7.510025 | 7.240365 | 18.528839 | 0 |
| 1 | segment_b | 3.394227 | 3.402080 | 8.272507 | 1 |
| 1 | segment_b | 2.550606 | 2.556617 | 6.122042 | 2 |
| 2 | segment_c | 2.711822 | 2.738570 | 4.532950 | 0 |
| 2 | segment_c | 4.593303 | 4.459809 | 8.320413 | 1 |
| 2 | segment_c | 4.944583 | 4.842134 | 9.074206 | 2 |
| 3 | segment_d | 5.825117 | 5.610581 | 51.673697 | 0 |
| 3 | segment_d | 4.951742 | 5.077606 | 39.208156 | 1 |
| 3 | segment_d | 5.460450 | 5.284514 | 46.397400 | 2 |
To summarize it we will take mean value of SMAPE metric because it is scale tolerant.
[25]:
score = metrics_deepar["SMAPE"].mean()
print(f"Average SMAPE for DeepAR: {score:.3f}")
Average SMAPE for DeepAR: 4.852
Visualize results.
[26]:
plot_backtest(forecast_deepar, ts, history_len=20)
3.3 DeepARNative#
It is recommended to use our native implementation of DeepAR, we will remove Pytorch Forecasting version in etna 3.0.0.
[27]:
from etna.models.nn import DeepARNativeModel
[28]:
num_lags = 7
scaler = StandardScalerTransform(in_column="target")
transform_date = DateFlagsTransform(
day_number_in_week=True,
day_number_in_month=False,
day_number_in_year=False,
week_number_in_month=False,
week_number_in_year=False,
month_number_in_year=False,
season_number=False,
year_number=False,
is_weekend=False,
out_column="dateflag",
)
transform_lag = LagTransform(
in_column="target",
lags=[HORIZON + i for i in range(num_lags)],
out_column="target_lag",
)
label_encoder = LabelEncoderTransform(
in_column="dateflag_day_number_in_week", strategy="none", out_column="dateflag_day_number_in_week_label"
)
embedding_sizes = {"dateflag_day_number_in_week_label": (7, 7)}
[29]:
set_seed()
model_deepar_native = DeepARNativeModel(
input_size=num_lags + 1,
encoder_length=2 * HORIZON,
decoder_length=HORIZON,
embedding_sizes=embedding_sizes,
lr=0.01,
scale=False,
n_samples=100,
trainer_params=dict(max_epochs=2),
)
pipeline_deepar_native = Pipeline(
model=model_deepar_native,
horizon=HORIZON,
transforms=[scaler, transform_lag, transform_date, label_encoder],
)
[30]:
metrics_deepar_native, forecast_deepar_native, fold_info_deepar_native = pipeline_deepar_native.backtest(
ts, metrics=metrics, n_folds=3, n_jobs=1
)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------
0 | loss | GaussianLoss | 0
1 | embedding | MultiEmbedding | 56
2 | rnn | LSTM | 4.3 K
3 | projection | ModuleDict | 34
----------------------------------------------
4.4 K Trainable params
0 Non-trainable params
4.4 K Total params
0.018 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=2` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 3.3s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------
0 | loss | GaussianLoss | 0
1 | embedding | MultiEmbedding | 56
2 | rnn | LSTM | 4.3 K
3 | projection | ModuleDict | 34
----------------------------------------------
4.4 K Trainable params
0 Non-trainable params
4.4 K Total params
0.018 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=2` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 6.9s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------
0 | loss | GaussianLoss | 0
1 | embedding | MultiEmbedding | 56
2 | rnn | LSTM | 4.3 K
3 | projection | ModuleDict | 34
----------------------------------------------
4.4 K Trainable params
0 Non-trainable params
4.4 K Total params
0.018 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=2` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 10.5s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 10.5s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.9s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 1.8s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 2.7s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 2.7s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[31]:
score = metrics_deepar_native["SMAPE"].mean()
print(f"Average SMAPE for DeepARNative: {score:.3f}")
Average SMAPE for DeepARNative: 5.816
[32]:
plot_backtest(forecast_deepar_native, ts, history_len=20)
3.4 TFT#
Let’s move to the next model.
[33]:
from etna.models.nn import TFTModel
[34]:
set_seed()
Default way#
[35]:
model_tft = TFTModel(
encoder_length=2 * HORIZON,
decoder_length=HORIZON,
trainer_params=dict(max_epochs=60, gradient_clip_val=0.1),
lr=0.01,
train_batch_size=32,
)
pipeline_tft = Pipeline(
model=model_tft,
horizon=HORIZON,
)
[36]:
metrics_tft, forecast_tft, fold_info_tft = pipeline_tft.backtest(ts, metrics=metrics, n_folds=3, n_jobs=1)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------------------------------------------------
0 | loss | QuantileLoss | 0
1 | logging_metrics | ModuleList | 0
2 | input_embeddings | MultiEmbedding | 0
3 | prescalers | ModuleDict | 96
4 | static_variable_selection | VariableSelectionNetwork | 1.7 K
5 | encoder_variable_selection | VariableSelectionNetwork | 1.8 K
6 | decoder_variable_selection | VariableSelectionNetwork | 1.2 K
7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K
8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K
9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K
10 | static_context_enrichment | GatedResidualNetwork | 1.1 K
11 | lstm_encoder | LSTM | 2.2 K
12 | lstm_decoder | LSTM | 2.2 K
13 | post_lstm_gate_encoder | GatedLinearUnit | 544
14 | post_lstm_add_norm_encoder | AddNorm | 32
15 | static_enrichment | GatedResidualNetwork | 1.4 K
16 | multihead_attn | InterpretableMultiHeadAttention | 676
17 | post_attn_gate_norm | GateAddNorm | 576
18 | pos_wise_ff | GatedResidualNetwork | 1.1 K
19 | pre_output_gate_norm | GateAddNorm | 576
20 | output_layer | Linear | 119
----------------------------------------------------------------------------------------
18.4 K Trainable params
0 Non-trainable params
18.4 K Total params
0.074 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=60` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 2.3min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------------------------------------------------
0 | loss | QuantileLoss | 0
1 | logging_metrics | ModuleList | 0
2 | input_embeddings | MultiEmbedding | 0
3 | prescalers | ModuleDict | 96
4 | static_variable_selection | VariableSelectionNetwork | 1.7 K
5 | encoder_variable_selection | VariableSelectionNetwork | 1.8 K
6 | decoder_variable_selection | VariableSelectionNetwork | 1.2 K
7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K
8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K
9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K
10 | static_context_enrichment | GatedResidualNetwork | 1.1 K
11 | lstm_encoder | LSTM | 2.2 K
12 | lstm_decoder | LSTM | 2.2 K
13 | post_lstm_gate_encoder | GatedLinearUnit | 544
14 | post_lstm_add_norm_encoder | AddNorm | 32
15 | static_enrichment | GatedResidualNetwork | 1.4 K
16 | multihead_attn | InterpretableMultiHeadAttention | 676
17 | post_attn_gate_norm | GateAddNorm | 576
18 | pos_wise_ff | GatedResidualNetwork | 1.1 K
19 | pre_output_gate_norm | GateAddNorm | 576
20 | output_layer | Linear | 119
----------------------------------------------------------------------------------------
18.4 K Trainable params
0 Non-trainable params
18.4 K Total params
0.074 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=60` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 4.8min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------------------------------------------------
0 | loss | QuantileLoss | 0
1 | logging_metrics | ModuleList | 0
2 | input_embeddings | MultiEmbedding | 0
3 | prescalers | ModuleDict | 96
4 | static_variable_selection | VariableSelectionNetwork | 1.7 K
5 | encoder_variable_selection | VariableSelectionNetwork | 1.8 K
6 | decoder_variable_selection | VariableSelectionNetwork | 1.2 K
7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K
8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K
9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K
10 | static_context_enrichment | GatedResidualNetwork | 1.1 K
11 | lstm_encoder | LSTM | 2.2 K
12 | lstm_decoder | LSTM | 2.2 K
13 | post_lstm_gate_encoder | GatedLinearUnit | 544
14 | post_lstm_add_norm_encoder | AddNorm | 32
15 | static_enrichment | GatedResidualNetwork | 1.4 K
16 | multihead_attn | InterpretableMultiHeadAttention | 676
17 | post_attn_gate_norm | GateAddNorm | 576
18 | pos_wise_ff | GatedResidualNetwork | 1.1 K
19 | pre_output_gate_norm | GateAddNorm | 576
20 | output_layer | Linear | 119
----------------------------------------------------------------------------------------
18.4 K Trainable params
0 Non-trainable params
18.4 K Total params
0.074 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=60` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 7.3min
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 7.3min
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[37]:
metrics_tft
[37]:
| segment | SMAPE | MAPE | MAE | fold_number | |
|---|---|---|---|---|---|
| 0 | segment_a | 48.136045 | 38.466858 | 207.882860 | 0 |
| 0 | segment_a | 15.005679 | 13.808033 | 75.980979 | 1 |
| 0 | segment_a | 78.258300 | 56.042976 | 307.516689 | 2 |
| 1 | segment_b | 25.119316 | 29.855394 | 70.259988 | 0 |
| 1 | segment_b | 11.968204 | 11.546382 | 28.715138 | 1 |
| 1 | segment_b | 10.453402 | 10.686422 | 24.270442 | 2 |
| 2 | segment_c | 60.551222 | 87.741155 | 148.974278 | 0 |
| 2 | segment_c | 10.676096 | 10.220948 | 18.654946 | 1 |
| 2 | segment_c | 25.062330 | 29.356065 | 51.625296 | 2 |
| 3 | segment_d | 89.548155 | 61.552517 | 536.311436 | 0 |
| 3 | segment_d | 39.707204 | 32.576548 | 273.843384 | 1 |
| 3 | segment_d | 111.788257 | 71.553335 | 610.088037 | 2 |
[38]:
score = metrics_tft["SMAPE"].mean()
print(f"Average SMAPE for TFT: {score:.3f}")
Average SMAPE for TFT: 43.856
Dataset Builder#
[39]:
set_seed()
num_lags = 10
lag_columns = [f"target_lag_{HORIZON+i}" for i in range(num_lags)]
transform_date = DateFlagsTransform(day_number_in_week=True, day_number_in_month=False, out_column="dateflag")
transform_lag = LagTransform(
in_column="target",
lags=[HORIZON + i for i in range(num_lags)],
out_column="target_lag",
)
dataset_builder_tft = PytorchForecastingDatasetBuilder(
max_encoder_length=HORIZON,
max_prediction_length=HORIZON,
time_varying_known_reals=["time_idx"],
time_varying_unknown_reals=["target"],
time_varying_known_categoricals=["dateflag_day_number_in_week"],
static_categoricals=["segment"],
target_normalizer=GroupNormalizer(groups=["segment"]),
)
[40]:
model_tft = TFTModel(
dataset_builder=dataset_builder_tft,
trainer_params=dict(max_epochs=50, gradient_clip_val=0.1),
lr=0.01,
train_batch_size=32,
)
pipeline_tft = Pipeline(
model=model_tft,
horizon=HORIZON,
transforms=[transform_lag, transform_date],
)
[41]:
metrics_tft, forecast_tft, fold_info_tft = pipeline_tft.backtest(ts, metrics=metrics, n_folds=3, n_jobs=1)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------------------------------------------------
0 | loss | QuantileLoss | 0
1 | logging_metrics | ModuleList | 0
2 | input_embeddings | MultiEmbedding | 47
3 | prescalers | ModuleDict | 96
4 | static_variable_selection | VariableSelectionNetwork | 1.8 K
5 | encoder_variable_selection | VariableSelectionNetwork | 1.9 K
6 | decoder_variable_selection | VariableSelectionNetwork | 1.3 K
7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K
8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K
9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K
10 | static_context_enrichment | GatedResidualNetwork | 1.1 K
11 | lstm_encoder | LSTM | 2.2 K
12 | lstm_decoder | LSTM | 2.2 K
13 | post_lstm_gate_encoder | GatedLinearUnit | 544
14 | post_lstm_add_norm_encoder | AddNorm | 32
15 | static_enrichment | GatedResidualNetwork | 1.4 K
16 | multihead_attn | InterpretableMultiHeadAttention | 676
17 | post_attn_gate_norm | GateAddNorm | 576
18 | pos_wise_ff | GatedResidualNetwork | 1.1 K
19 | pre_output_gate_norm | GateAddNorm | 576
20 | output_layer | Linear | 119
----------------------------------------------------------------------------------------
18.9 K Trainable params
0 Non-trainable params
18.9 K Total params
0.075 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=50` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 2.5min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------------------------------------------------
0 | loss | QuantileLoss | 0
1 | logging_metrics | ModuleList | 0
2 | input_embeddings | MultiEmbedding | 47
3 | prescalers | ModuleDict | 96
4 | static_variable_selection | VariableSelectionNetwork | 1.8 K
5 | encoder_variable_selection | VariableSelectionNetwork | 1.9 K
6 | decoder_variable_selection | VariableSelectionNetwork | 1.3 K
7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K
8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K
9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K
10 | static_context_enrichment | GatedResidualNetwork | 1.1 K
11 | lstm_encoder | LSTM | 2.2 K
12 | lstm_decoder | LSTM | 2.2 K
13 | post_lstm_gate_encoder | GatedLinearUnit | 544
14 | post_lstm_add_norm_encoder | AddNorm | 32
15 | static_enrichment | GatedResidualNetwork | 1.4 K
16 | multihead_attn | InterpretableMultiHeadAttention | 676
17 | post_attn_gate_norm | GateAddNorm | 576
18 | pos_wise_ff | GatedResidualNetwork | 1.1 K
19 | pre_output_gate_norm | GateAddNorm | 576
20 | output_layer | Linear | 119
----------------------------------------------------------------------------------------
18.9 K Trainable params
0 Non-trainable params
18.9 K Total params
0.075 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=50` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 5.2min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------------------------------------------------
0 | loss | QuantileLoss | 0
1 | logging_metrics | ModuleList | 0
2 | input_embeddings | MultiEmbedding | 47
3 | prescalers | ModuleDict | 96
4 | static_variable_selection | VariableSelectionNetwork | 1.8 K
5 | encoder_variable_selection | VariableSelectionNetwork | 1.9 K
6 | decoder_variable_selection | VariableSelectionNetwork | 1.3 K
7 | static_context_variable_selection | GatedResidualNetwork | 1.1 K
8 | static_context_initial_hidden_lstm | GatedResidualNetwork | 1.1 K
9 | static_context_initial_cell_lstm | GatedResidualNetwork | 1.1 K
10 | static_context_enrichment | GatedResidualNetwork | 1.1 K
11 | lstm_encoder | LSTM | 2.2 K
12 | lstm_decoder | LSTM | 2.2 K
13 | post_lstm_gate_encoder | GatedLinearUnit | 544
14 | post_lstm_add_norm_encoder | AddNorm | 32
15 | static_enrichment | GatedResidualNetwork | 1.4 K
16 | multihead_attn | InterpretableMultiHeadAttention | 676
17 | post_attn_gate_norm | GateAddNorm | 576
18 | pos_wise_ff | GatedResidualNetwork | 1.1 K
19 | pre_output_gate_norm | GateAddNorm | 576
20 | output_layer | Linear | 119
----------------------------------------------------------------------------------------
18.9 K Trainable params
0 Non-trainable params
18.9 K Total params
0.075 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=50` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 7.8min
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 7.8min
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.4s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.6s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.6s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[42]:
metrics_tft
[42]:
| segment | SMAPE | MAPE | MAE | fold_number | |
|---|---|---|---|---|---|
| 0 | segment_a | 6.116235 | 5.901792 | 31.657100 | 0 |
| 0 | segment_a | 6.543858 | 6.257777 | 33.911211 | 1 |
| 0 | segment_a | 7.527751 | 7.201543 | 39.835205 | 2 |
| 1 | segment_b | 6.692551 | 6.420435 | 16.839042 | 0 |
| 1 | segment_b | 6.789591 | 6.519271 | 16.461197 | 1 |
| 1 | segment_b | 5.155557 | 4.980205 | 12.515908 | 2 |
| 2 | segment_c | 3.931971 | 3.846683 | 6.791909 | 0 |
| 2 | segment_c | 3.867445 | 3.760706 | 7.001602 | 1 |
| 2 | segment_c | 6.231263 | 5.934746 | 11.344711 | 2 |
| 3 | segment_d | 7.338428 | 7.018524 | 64.302726 | 0 |
| 3 | segment_d | 3.332532 | 3.341312 | 26.611154 | 1 |
| 3 | segment_d | 4.325608 | 4.201900 | 36.394749 | 2 |
[43]:
score = metrics_tft["SMAPE"].mean()
print(f"Average SMAPE for TFT: {score:.3f}")
Average SMAPE for TFT: 5.654
[44]:
plot_backtest(forecast_tft, ts, history_len=20)
3.5 TFTNative#
It is recommended to use our native implementation of TFT, we will remove Pytorch Forecasting version in etna 3.0.0.
[45]:
from etna.models.nn import TFTNativeModel
[46]:
num_lags = 7
lag_columns = [f"target_lag_{HORIZON+i}" for i in range(num_lags)]
transform_lag = LagTransform(
in_column="target",
lags=[HORIZON + i for i in range(num_lags)],
out_column="target_lag",
)
transform_date = DateFlagsTransform(
day_number_in_week=True,
day_number_in_month=False,
day_number_in_year=False,
week_number_in_month=False,
week_number_in_year=False,
month_number_in_year=False,
season_number=False,
year_number=False,
is_weekend=False,
out_column="dateflag",
)
scaler = StandardScalerTransform(in_column=["target"])
encoder = SegmentEncoderTransform()
label_encoder = LabelEncoderTransform(
in_column="dateflag_day_number_in_week", strategy="none", out_column="dateflag_day_number_in_week_label"
)
[47]:
set_seed()
model_tft_native = TFTNativeModel(
encoder_length=2 * HORIZON,
decoder_length=HORIZON,
static_categoricals=["segment_code"],
time_varying_categoricals_encoder=["dateflag_day_number_in_week_label"],
time_varying_categoricals_decoder=["dateflag_day_number_in_week_label"],
time_varying_reals_encoder=["target"] + lag_columns,
time_varying_reals_decoder=lag_columns,
num_embeddings={"segment_code": len(ts.segments), "dateflag_day_number_in_week_label": 7},
n_heads=1,
num_layers=2,
hidden_size=32,
lr=0.0001,
train_batch_size=16,
trainer_params=dict(max_epochs=5, gradient_clip_val=0.1),
)
pipeline_tft_native = Pipeline(
model=model_tft_native, horizon=HORIZON, transforms=[transform_lag, scaler, transform_date, encoder, label_encoder]
)
[48]:
metrics_tft_native, forecast_tft_native, fold_info_tft_native = pipeline_tft_native.backtest(
ts, metrics=metrics, n_folds=3, n_jobs=1
)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------------------------------------------
0 | loss | MSELoss | 0
1 | static_scalers | ModuleDict | 0
2 | static_embeddings | ModuleDict | 160
3 | time_varying_scalers_encoder | ModuleDict | 512
4 | time_varying_embeddings_encoder | ModuleDict | 256
5 | time_varying_scalers_decoder | ModuleDict | 448
6 | time_varying_embeddings_decoder | ModuleDict | 256
7 | static_variable_selection | VariableSelectionNetwork | 6.5 K
8 | encoder_variable_selection | VariableSelectionNetwork | 222 K
9 | decoder_variable_selection | VariableSelectionNetwork | 180 K
10 | static_covariate_encoder | StaticCovariateEncoder | 17.2 K
11 | lstm_encoder | LSTM | 16.9 K
12 | lstm_decoder | LSTM | 16.9 K
13 | gated_norm1 | GateAddNorm | 2.2 K
14 | temporal_fusion_decoder | TemporalFusionDecoder | 16.0 K
15 | gated_norm2 | GateAddNorm | 2.2 K
16 | output_fc | Linear | 33
------------------------------------------------------------------------------
481 K Trainable params
0 Non-trainable params
481 K Total params
1.927 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 37.2s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------------------------------------------
0 | loss | MSELoss | 0
1 | static_scalers | ModuleDict | 0
2 | static_embeddings | ModuleDict | 160
3 | time_varying_scalers_encoder | ModuleDict | 512
4 | time_varying_embeddings_encoder | ModuleDict | 256
5 | time_varying_scalers_decoder | ModuleDict | 448
6 | time_varying_embeddings_decoder | ModuleDict | 256
7 | static_variable_selection | VariableSelectionNetwork | 6.5 K
8 | encoder_variable_selection | VariableSelectionNetwork | 222 K
9 | decoder_variable_selection | VariableSelectionNetwork | 180 K
10 | static_covariate_encoder | StaticCovariateEncoder | 17.2 K
11 | lstm_encoder | LSTM | 16.9 K
12 | lstm_decoder | LSTM | 16.9 K
13 | gated_norm1 | GateAddNorm | 2.2 K
14 | temporal_fusion_decoder | TemporalFusionDecoder | 16.0 K
15 | gated_norm2 | GateAddNorm | 2.2 K
16 | output_fc | Linear | 33
------------------------------------------------------------------------------
481 K Trainable params
0 Non-trainable params
481 K Total params
1.927 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 1.3min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------------------------------------------
0 | loss | MSELoss | 0
1 | static_scalers | ModuleDict | 0
2 | static_embeddings | ModuleDict | 160
3 | time_varying_scalers_encoder | ModuleDict | 512
4 | time_varying_embeddings_encoder | ModuleDict | 256
5 | time_varying_scalers_decoder | ModuleDict | 448
6 | time_varying_embeddings_decoder | ModuleDict | 256
7 | static_variable_selection | VariableSelectionNetwork | 6.5 K
8 | encoder_variable_selection | VariableSelectionNetwork | 222 K
9 | decoder_variable_selection | VariableSelectionNetwork | 180 K
10 | static_covariate_encoder | StaticCovariateEncoder | 17.2 K
11 | lstm_encoder | LSTM | 16.9 K
12 | lstm_decoder | LSTM | 16.9 K
13 | gated_norm1 | GateAddNorm | 2.2 K
14 | temporal_fusion_decoder | TemporalFusionDecoder | 16.0 K
15 | gated_norm2 | GateAddNorm | 2.2 K
16 | output_fc | Linear | 33
------------------------------------------------------------------------------
481 K Trainable params
0 Non-trainable params
481 K Total params
1.927 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 1.9min
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 1.9min
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.4s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.5s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.5s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[49]:
score = metrics_tft_native["SMAPE"].mean()
print(f"Average SMAPE for TFTNative: {score:.3f}")
Average SMAPE for TFTNative: 6.151
[50]:
plot_backtest(forecast_tft_native, ts, history_len=20)
3.6 RNN#
We’ll use RNN model based on LSTM cell
[51]:
from etna.models.nn import RNNModel
[52]:
num_lags = 7
scaler = StandardScalerTransform(in_column="target")
transform_lag = LagTransform(
in_column="target",
lags=[HORIZON + i for i in range(num_lags)],
out_column="target_lag",
)
transform_date = DateFlagsTransform(
day_number_in_week=True,
day_number_in_month=False,
day_number_in_year=False,
week_number_in_month=False,
week_number_in_year=False,
month_number_in_year=False,
season_number=False,
year_number=False,
is_weekend=False,
out_column="dateflag",
)
segment_encoder = SegmentEncoderTransform()
label_encoder = LabelEncoderTransform(
in_column="dateflag_day_number_in_week", strategy="none", out_column="dateflag_day_number_in_week_label"
)
embedding_sizes = {"dateflag_day_number_in_week_label": (7, 7)}
[53]:
set_seed()
model_rnn = RNNModel(
input_size=num_lags + 1,
encoder_length=2 * HORIZON,
decoder_length=HORIZON,
embedding_sizes=embedding_sizes,
trainer_params=dict(max_epochs=5),
lr=1e-3,
)
pipeline_rnn = Pipeline(
model=model_rnn,
horizon=HORIZON,
transforms=[scaler, transform_lag, transform_date, label_encoder],
)
[54]:
metrics_rnn, forecast_rnn, fold_info_rnn = pipeline_rnn.backtest(ts, metrics=metrics, n_folds=3, n_jobs=1)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------
0 | loss | MSELoss | 0
1 | embedding | MultiEmbedding | 56
2 | rnn | LSTM | 4.3 K
3 | projection | Linear | 17
----------------------------------------------
4.4 K Trainable params
0 Non-trainable params
4.4 K Total params
0.017 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 7.9s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------
0 | loss | MSELoss | 0
1 | embedding | MultiEmbedding | 56
2 | rnn | LSTM | 4.3 K
3 | projection | Linear | 17
----------------------------------------------
4.4 K Trainable params
0 Non-trainable params
4.4 K Total params
0.017 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 15.8s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------
0 | loss | MSELoss | 0
1 | embedding | MultiEmbedding | 56
2 | rnn | LSTM | 4.3 K
3 | projection | Linear | 17
----------------------------------------------
4.4 K Trainable params
0 Non-trainable params
4.4 K Total params
0.017 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 23.7s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 23.7s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.5s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.5s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[55]:
score = metrics_rnn["SMAPE"].mean()
print(f"Average SMAPE for LSTM: {score:.3f}")
Average SMAPE for LSTM: 5.653
[56]:
plot_backtest(forecast_rnn, ts, history_len=20)
3.7 MLP#
Base model with linear layers and activations.
[57]:
from etna.models.nn import MLPModel
[58]:
num_lags = 14
transform_lag = LagTransform(
in_column="target",
lags=[HORIZON + i for i in range(num_lags)],
out_column="target_lag",
)
transform_date = DateFlagsTransform(
day_number_in_week=True,
day_number_in_month=False,
day_number_in_year=False,
week_number_in_month=False,
week_number_in_year=False,
month_number_in_year=False,
season_number=False,
year_number=False,
is_weekend=False,
out_column="dateflag",
)
segment_encoder = SegmentEncoderTransform()
label_encoder = LabelEncoderTransform(
in_column="dateflag_day_number_in_week", strategy="none", out_column="dateflag_day_number_in_week_label"
)
embedding_sizes = {"dateflag_day_number_in_week_label": (7, 7)}
[59]:
set_seed()
model_mlp = MLPModel(
input_size=num_lags,
hidden_size=[16],
embedding_sizes=embedding_sizes,
decoder_length=HORIZON,
trainer_params=dict(max_epochs=50, gradient_clip_val=0.1),
lr=0.001,
train_batch_size=16,
)
metrics = [SMAPE(), MAPE(), MAE()]
pipeline_mlp = Pipeline(model=model_mlp, transforms=[transform_lag, transform_date, label_encoder], horizon=HORIZON)
[60]:
metrics_mlp, forecast_mlp, fold_info_mlp = pipeline_mlp.backtest(ts, metrics=metrics, n_folds=3, n_jobs=1)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
---------------------------------------------
0 | loss | MSELoss | 0
1 | embedding | MultiEmbedding | 56
2 | mlp | Sequential | 369
---------------------------------------------
425 Trainable params
0 Non-trainable params
425 Total params
0.002 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=50` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 4.8s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
---------------------------------------------
0 | loss | MSELoss | 0
1 | embedding | MultiEmbedding | 56
2 | mlp | Sequential | 369
---------------------------------------------
425 Trainable params
0 Non-trainable params
425 Total params
0.002 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=50` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 9.8s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
---------------------------------------------
0 | loss | MSELoss | 0
1 | embedding | MultiEmbedding | 56
2 | mlp | Sequential | 369
---------------------------------------------
425 Trainable params
0 Non-trainable params
425 Total params
0.002 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=50` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 14.8s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 14.8s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.4s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.4s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[61]:
score = metrics_mlp["SMAPE"].mean()
print(f"Average SMAPE for MLP: {score:.3f}")
Average SMAPE for MLP: 5.970
[62]:
plot_backtest(forecast_mlp, ts, history_len=20)
3.8 Deep State Model#
Deep State Model works well with multiple similar time-series. It inffers shared patterns from them.
We have to determine the type of seasonality in data (based on data granularity), SeasonalitySSM class is responsible for this. In this example, we have daily data, so we use day-of-week (7 seasons) and day-of-month (31 seasons) models. We also set the trend component using the LevelTrendSSM class. Also in the model we use time-based features like day-of-week, day-of-month and time independent feature representing the segment of time series.
[63]:
from etna.models.nn import DeepStateModel
from etna.models.nn.deepstate import CompositeSSM
from etna.models.nn.deepstate import LevelTrendSSM
from etna.models.nn.deepstate import SeasonalitySSM
[64]:
from etna.transforms import FilterFeaturesTransform
[65]:
num_lags = 7
transforms = [
SegmentEncoderTransform(),
StandardScalerTransform(in_column="target"),
DateFlagsTransform(
day_number_in_week=True,
day_number_in_month=True,
day_number_in_year=False,
week_number_in_month=False,
week_number_in_year=False,
month_number_in_year=False,
season_number=False,
year_number=False,
is_weekend=False,
out_column="dateflag",
),
LagTransform(
in_column="target",
lags=[HORIZON + i for i in range(num_lags)],
out_column="target_lag",
),
LabelEncoderTransform(
in_column="dateflag_day_number_in_week", strategy="none", out_column="dateflag_day_number_in_week_label"
),
LabelEncoderTransform(
in_column="dateflag_day_number_in_month", strategy="none", out_column="dateflag_day_number_in_month_label"
),
FilterFeaturesTransform(exclude=["dateflag_day_number_in_week", "dateflag_day_number_in_month"]),
]
embedding_sizes = {
"dateflag_day_number_in_week_label": (7, 7),
"dateflag_day_number_in_month_label": (31, 7),
"segment_code": (4, 7),
}
[66]:
monthly_smm = SeasonalitySSM(num_seasons=31, timestamp_transform=lambda x: x.day - 1)
weekly_smm = SeasonalitySSM(num_seasons=7, timestamp_transform=lambda x: x.weekday())
[67]:
set_seed()
model_dsm = DeepStateModel(
ssm=CompositeSSM(seasonal_ssms=[weekly_smm, monthly_smm], nonseasonal_ssm=LevelTrendSSM()),
input_size=num_lags,
encoder_length=2 * HORIZON,
decoder_length=HORIZON,
embedding_sizes=embedding_sizes,
trainer_params=dict(max_epochs=5),
lr=1e-3,
)
pipeline_dsm = Pipeline(
model=model_dsm,
horizon=HORIZON,
transforms=transforms,
)
[68]:
metrics_dsm, forecast_dsm, fold_info_dsm = pipeline_dsm.backtest(ts, metrics=metrics, n_folds=3, n_jobs=1)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------
0 | embedding | MultiEmbedding | 315
1 | RNN | LSTM | 11.2 K
2 | projectors | ModuleDict | 5.0 K
----------------------------------------------
16.5 K Trainable params
0 Non-trainable params
16.5 K Total params
0.066 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 19.9s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------
0 | embedding | MultiEmbedding | 315
1 | RNN | LSTM | 11.2 K
2 | projectors | ModuleDict | 5.0 K
----------------------------------------------
16.5 K Trainable params
0 Non-trainable params
16.5 K Total params
0.066 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 41.5s
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
----------------------------------------------
0 | embedding | MultiEmbedding | 315
1 | RNN | LSTM | 11.2 K
2 | projectors | ModuleDict | 5.0 K
----------------------------------------------
16.5 K Trainable params
0 Non-trainable params
16.5 K Total params
0.066 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=5` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 1.0min
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 1.0min
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.5s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.8s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.8s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[69]:
score = metrics_dsm["SMAPE"].mean()
print(f"Average SMAPE for DeepStateModel: {score:.3f}")
Average SMAPE for DeepStateModel: 5.520
[70]:
plot_backtest(forecast_dsm, ts, history_len=20)
3.9 N-BEATS Model#
This architecture is based on backward and forward residual links and a deep stack of fully connected layers.
There are two types of models in the library. The NBeatsGenericModel class implements a generic deep learning model, while the NBeatsInterpretableModel is augmented with certain inductive biases to be interpretable (trend and seasonality).
[71]:
from etna.models.nn import NBeatsGenericModel
from etna.models.nn import NBeatsInterpretableModel
[72]:
set_seed()
model_nbeats_generic = NBeatsGenericModel(
input_size=2 * HORIZON,
output_size=HORIZON,
loss="smape",
stacks=30,
layers=4,
layer_size=256,
trainer_params=dict(max_epochs=1000),
lr=1e-3,
)
pipeline_nbeats_generic = Pipeline(
model=model_nbeats_generic,
horizon=HORIZON,
transforms=[],
)
[73]:
metrics_nbeats_generic, forecast_nbeats_generic, _ = pipeline_nbeats_generic.backtest(
ts, metrics=metrics, n_folds=3, n_jobs=1
)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
--------------------------------------
0 | model | NBeats | 206 K
1 | loss | NBeatsSMAPE | 0
--------------------------------------
206 K Trainable params
0 Non-trainable params
206 K Total params
0.826 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=1000` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 1.4min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
--------------------------------------
0 | model | NBeats | 206 K
1 | loss | NBeatsSMAPE | 0
--------------------------------------
206 K Trainable params
0 Non-trainable params
206 K Total params
0.826 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=1000` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 2.7min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
--------------------------------------
0 | model | NBeats | 206 K
1 | loss | NBeatsSMAPE | 0
--------------------------------------
206 K Trainable params
0 Non-trainable params
206 K Total params
0.826 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=1000` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 4.1min
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 4.1min
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[74]:
score = metrics_nbeats_generic["SMAPE"].mean()
print(f"Average SMAPE for N-BEATS Generic: {score:.3f}")
Average SMAPE for N-BEATS Generic: 5.674
[75]:
plot_backtest(forecast_nbeats_generic, ts, history_len=20)
[76]:
model_nbeats_interp = NBeatsInterpretableModel(
input_size=4 * HORIZON,
output_size=HORIZON,
loss="smape",
trend_layer_size=64,
seasonality_layer_size=256,
trainer_params=dict(max_epochs=2000),
lr=1e-3,
)
pipeline_nbeats_interp = Pipeline(
model=model_nbeats_interp,
horizon=HORIZON,
transforms=[],
)
[77]:
metrics_nbeats_interp, forecast_nbeats_interp, _ = pipeline_nbeats_interp.backtest(
ts, metrics=metrics, n_folds=3, n_jobs=1
)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
--------------------------------------
0 | model | NBeats | 224 K
1 | loss | NBeatsSMAPE | 0
--------------------------------------
223 K Trainable params
385 Non-trainable params
224 K Total params
0.896 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=2000` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 1.6min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
--------------------------------------
0 | model | NBeats | 224 K
1 | loss | NBeatsSMAPE | 0
--------------------------------------
223 K Trainable params
385 Non-trainable params
224 K Total params
0.896 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=2000` reached.
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 3.2min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
--------------------------------------
0 | model | NBeats | 224 K
1 | loss | NBeatsSMAPE | 0
--------------------------------------
223 K Trainable params
385 Non-trainable params
224 K Total params
0.896 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=2000` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 4.8min
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 4.8min
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[78]:
score = metrics_nbeats_interp["SMAPE"].mean()
print(f"Average SMAPE for N-BEATS Interpretable: {score:.3f}")
Average SMAPE for N-BEATS Interpretable: 5.441
[79]:
plot_backtest(forecast_nbeats_interp, ts, history_len=20)
3.10 PatchTS Model#
Model with transformer encoder that uses patches of timeseries as input words and linear decoder.
[80]:
from etna.models.nn import PatchTSModel
[81]:
set_seed()
model_patchts = PatchTSModel(
decoder_length=HORIZON,
encoder_length=2 * HORIZON,
patch_len=1,
trainer_params=dict(max_epochs=30),
lr=1e-3,
train_batch_size=64,
)
pipeline_patchts = Pipeline(
model=model_patchts, horizon=HORIZON, transforms=[StandardScalerTransform(in_column="target")]
)
metrics_patchts, forecast_patchts, fold_info_patchs = pipeline_patchts.backtest(
ts, metrics=metrics, n_folds=3, n_jobs=1
)
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------
0 | loss | MSELoss | 0
1 | model | Sequential | 397 K
2 | projection | Sequential | 1.8 K
------------------------------------------
399 K Trainable params
0 Non-trainable params
399 K Total params
1.598 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=30` reached.
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 3.7min
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
------------------------------------------
0 | loss | MSELoss | 0
1 | model | Sequential | 397 K
2 | projection | Sequential | 1.8 K
------------------------------------------
399 K Trainable params
0 Non-trainable params
399 K Total params
1.598 Total estimated model params size (MB)
`Trainer.fit` stopped: `max_epochs=30` reached.
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 11.5min
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 11.5min
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.2s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.3s
[Parallel(n_jobs=1)]: Done 1 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 2 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 3 tasks | elapsed: 0.1s
[82]:
score = metrics_patchts["SMAPE"].mean()
print(f"Average SMAPE for PatchTS: {score:.3f}")
Average SMAPE for PatchTS: 5.852
[83]:
plot_backtest(forecast_patchts, ts, history_len=20)
