skforecast
Time series forecasting with scikit-learn regressors.
Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM, XGBoost, Ranger...).
Documentation: https://joaquinamatrodrigo.github.io/skforecast/
Version 0.4 has undergone a huge code refactoring. Main changes are related to input-output formats (only pandas series and dataframes are allowed although internally numpy arrays are used for performance) and model validation methods (unified into backtesting with and without refit). Changelog
Table of contents
Installation
pip install skforecast
Specific version:
pip install skforecast==0.4.1
Latest (unstable):
pip install git+https://github.com/JoaquinAmatRodrigo/skforecast#master
The most common error when importing the library is:
'cannot import name 'mean_absolute_percentage_error' from 'sklearn.metrics'.
This is because the scikit-learn installation is lower than 0.24. Try to upgrade scikit-learn with
pip3 install -U scikit-learn
There is a current problem when installing statsmodels 0.13 in Google Colab. To avoid this dependency issues when installing skforecast:
pip install statsmodels==0.12.2
pip install skforecast
Dependencies
- numpy>=1.20.1
- pandas>=1.2.2
- tqdm>=4.57.0
- scikit-learn>=1.0.1
- statsmodels>=0.12.2
Features
- Create recursive autoregressive forecasters from any scikit-learn regressor
- Create multi-output autoregressive forecasters from any scikit-learn regressor
- Grid search to find optimal hyperparameters
- Grid search to find optimal lags (predictors)
- Include exogenous variables as predictors
- Include custom predictors (rolling mean, rolling variance ...)
- Backtesting
- Prediction interval estimated by bootstrapping
- Get predictor importance
Introduction
A time series is a sequence of data arranged chronologically, in principle, equally spaced in time. Time series forecasting is the use of a model to predict future values based on previously observed values, with the option of also including other external variables.
When working with time series, it is seldom needed to predict only the next element in the series (t+1). Instead, the most common goal is to predict a whole future interval (t+1, ..., t+n) or a far point in time (t+n). There are several strategies that allow generating this type of multiple predictions.
Recursive multi-step forecasting
Since the value of t(n) is required to predict the point t(n-1), and t(n-1) is unknown, it is necessary to make recursive predictions in which, each new prediction, is based on the previous one. This process is known as recursive forecasting or recursive multi-step forecasting.
The main challenge when using scikit-learn models for recursive multi-step forecasting is transforming the time series in an matrix where, each value of the series, is related to the time window (lags) that precedes it. This forecasting strategy can be easily generated with the classes ForecasterAutoreg and ForecasterAutoregCustom.
Direct multi-step forecasting
This strategy consists of training a different model for each step. For example, to predict the next 5 values of a time series, 5 different models are trainded, one for each step. As a result, the predictions are independent of each other. This forecasting strategy can be easily generated with the ForecasterAutoregMultiOutput class (changed in version 0.1.9).
Multiple output forecasting
Certain models are capable of simultaneously predicting several values of a sequence (one-shot), for example, LSTM neural networks. This strategy is not implemented in skforecast.
Getting started
Autoregressive forecaster
# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
# Download data
# ==============================================================================
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv')
data = pd.read_csv(url, sep=',', header=0, names=['y', 'datetime'])
# Data preprocessing
# ==============================================================================
data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d')
data = data.set_index('datetime')
data = data.asfreq('MS')
data = data['y']
data = data.sort_index()
# Split train-test
# ==============================================================================
steps = 36
data_train = data[:-steps]
data_test = data[-steps:]
# Plot
# ==============================================================================
fig, ax = plt.subplots(figsize=(9, 4))
data_train.plot(ax=ax, label='train')
data_test.plot(ax=ax, label='test')
ax.legend()
# Create and fit forecaster
# ==============================================================================
forecaster = ForecasterAutoreg(
regressor = RandomForestRegressor(random_state=123),
lags = 15
)
forecaster.fit(y=data_train)
forecaster
=================
ForecasterAutoreg
=================
Regressor: RandomForestRegressor(random_state=123)
Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]
Window size: 15
Included exogenous: False
Type of exogenous variable: None
Exogenous variables names: None
Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]
Training index type: DatetimeIndex
Training index frequency: MS
Regressor parameters: {'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False}
Creation date: 2022-01-02 16:50:21
Last fit date: 2022-01-02 16:50:21
Skforecast version: 0.4.2
# Predict
# ==============================================================================
predictions = forecaster.predict(steps=36)
predictions.head(3)
2005-07-01 0.921840 2005-08-01 0.954921 2005-09-01 1.101716 Freq: MS, Name: pred, dtype: float64
# Plot predictions
# ==============================================================================
fig, ax=plt.subplots(figsize=(9, 4))
data_train.plot(ax=ax, label='train')
data_test.plot(ax=ax, label='test')
predictions.plot(ax=ax, label='predictions')
ax.legend()
# Prediction error
# ==============================================================================
error_mse = mean_squared_error(
y_true = data_test,
y_pred = predictions
)
print(f"Test error (mse): {error_mse}")
Test error (mse): 0.00429855684785846
# Feature importance
# ==============================================================================
forecaster.get_feature_importance()
| feature | importance |
|-----------|--------------|
| lag_1 | 0.0123397 |
| lag_2 | 0.0851603 |
| lag_3 | 0.0134071 |
| lag_4 | 0.00437446 |
| lag_5 | 0.00318805 |
| lag_6 | 0.00343593 |
| lag_7 | 0.00313612 |
| lag_8 | 0.00714094 |
| lag_9 | 0.00783053 |
| lag_10 | 0.0127507 |
| lag_11 | 0.00901919 |
| lag_12 | 0.807098 |
| lag_13 | 0.00481128 |
| lag_14 | 0.0163282 |
| lag_15 | 0.0099792 |
Autoregressive forecaster with exogenous predictors
# Download data
# ==============================================================================
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o_exog.csv')
data = pd.read_csv(url, sep=',', header=0, names=['datetime', 'y', 'exog_1', 'exog_2'])
# Data preprocessing
# ==============================================================================
data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d')
data = data.set_index('datetime')
data = data.asfreq('MS')
data = data.sort_index()
# Plot
# ==============================================================================
fig, ax=plt.subplots(figsize=(9, 4))
data.plot(ax=ax);
# Split train-test
# ==============================================================================
steps = 36
data_train = data.iloc[:-steps, :]
data_test = data.iloc[-steps:, :]
# Create and fit forecaster
# ==============================================================================
forecaster = ForecasterAutoreg(
regressor = RandomForestRegressor(random_state=123),
lags = 15
)
forecaster.fit(
y = data_train['y'],
exog = data_train[['exog_1', 'exog_2']]
)
forecaster
=================
ForecasterAutoreg
=================
Regressor: RandomForestRegressor(random_state=123)
Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]
Window size: 15
Included exogenous: True
Type of exogenous variable:
Exogenous variables names: ['exog_1', 'exog_2']
Training range: [Timestamp('1992-04-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]
Training index type: DatetimeIndex
Training index frequency: MS
Regressor parameters: {'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False}
Creation date: 2022-01-02 16:51:34
Last fit date: 2022-01-02 16:51:34
Skforecast version: 0.4.2
# Feature importance
# ==============================================================================
forecaster.get_feature_importance()
| feature | importance |
|-----------|--------------|
| lag_1 | 0.0133541 |
| lag_2 | 0.0611202 |
| lag_3 | 0.00908617 |
| lag_4 | 0.00272094 |
| lag_5 | 0.00247847 |
| lag_6 | 0.00315493 |
| lag_7 | 0.00217887 |
| lag_8 | 0.00815443 |
| lag_9 | 0.0103189 |
| lag_10 | 0.0205869 |
| lag_11 | 0.00703555 |
| lag_12 | 0.773389 |
| lag_13 | 0.00458297 |
| lag_14 | 0.0181272 |
| lag_15 | 0.00873237 |
| exog_1 | 0.0103638 |
| exog_2 | 0.0446156 |
Autoregressive forecaster with custom predictors
# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skforecast.ForecasterAutoregCustom import ForecasterAutoregCustom
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
# Download data
# ==============================================================================
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv')
data = pd.read_csv(url, sep=',', header=0, names=['y', 'datetime'])
# Data preprocessing
# ==============================================================================
data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d')
data = data.set_index('datetime')
data = data.asfreq('MS')
data = data['y']
data = data.sort_index()
# Split train-test
# ==============================================================================
steps = 36
data_train = data[:-steps]
data_test = data[-steps:]
# Custom function to create predictors
# ==============================================================================
def create_predictors(y):
'''
Create first 10 lags of a time series.
Calculate moving average with window 20.
'''
lags = y[-1:-11:-1]
mean = np.mean(y[-20:])
predictors = np.hstack([lags, mean])
return predictors
# Create and fit forecaster
# ==============================================================================
forecaster = ForecasterAutoregCustom(
regressor = RandomForestRegressor(random_state=123),
fun_predictors = create_predictors,
window_size = 20
)
forecaster.fit(y=data_train)
forecaster
=======================
ForecasterAutoregCustom
=======================
Regressor: RandomForestRegressor(random_state=123)
Predictors created with function: create_predictors
Window size: 20
Included exogenous: False
Type of exogenous variable: None
Exogenous variables names: None
Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]
Training index type: DatetimeIndex
Training index frequency: MS
Regressor parameters: {'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 'auto', 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False}
Creation date: 2022-01-02 16:52:12
Last fit date: 2022-01-02 16:52:12
Skforecast version: 0.4.2
# Predict
# ==============================================================================
predictions = forecaster.predict(steps=36)
predictions.head(3)
2005-07-01 0.926598 2005-08-01 0.948202 2005-09-01 1.020947 Freq: MS, Name: pred, dtype: float64
# Feature importance
# ==============================================================================
forecaster.get_feature_importance()
| feature | importance |
|---------------------|--------------|
| custom_predictor_0 | 0.53972 |
| custom_predictor_1 | 0.119097 |
| custom_predictor_2 | 0.0464036 |
| custom_predictor_3 | 0.0241653 |
| custom_predictor_4 | 0.0305667 |
| custom_predictor_5 | 0.0151391 |
| custom_predictor_6 | 0.0428832 |
| custom_predictor_7 | 0.012742 |
| custom_predictor_8 | 0.018938 |
| custom_predictor_9 | 0.108639 |
| custom_predictor_10 | 0.0417066 |
Prediction intervals
# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
# Download data
# ==============================================================================
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv')
data = pd.read_csv(url, sep=',', header=0, names=['y', 'datetime'])
# Data preprocessing
# ==============================================================================
data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d')
data = data.set_index('datetime')
data = data.asfreq('MS')
data = data['y']
data = data.sort_index()
# Split train-test
# ==============================================================================
steps = 36
data_train = data[:-steps]
data_test = data[-steps:]
# Create and fit forecaster
# ==============================================================================
forecaster = ForecasterAutoreg(
regressor = make_pipeline(StandardScaler(), Ridge()),
lags = 15
)
forecaster.fit(y=data_train)
forecaster
=================
ForecasterAutoreg
=================
Regressor: Pipeline(steps=[('standardscaler', StandardScaler()), ('ridge', Ridge())])
Lags: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15]
Window size: 15
Included exogenous: False
Type of exogenous variable: None
Exogenous variables names: None
Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2005-06-01 00:00:00')]
Training index type: DatetimeIndex
Training index frequency: MS
Regressor parameters: {'standardscaler__copy': True, 'standardscaler__with_mean': True, 'standardscaler__with_std': True, 'ridge__alpha': 1.0, 'ridge__copy_X': True, 'ridge__fit_intercept': True, 'ridge__max_iter': None, 'ridge__normalize': 'deprecated', 'ridge__positive': False, 'ridge__random_state': None, 'ridge__solver': 'auto', 'ridge__tol': 0.001}
Creation date: 2022-01-02 16:53:00
Last fit date: 2022-01-02 16:53:00
Skforecast version: 0.4.2
# Prediction intervals
# ==============================================================================
predictions = forecaster.predict_interval(
steps = steps,
interval = [5, 95],
n_boot = 500
)
fig, ax=plt.subplots(figsize=(9, 4))
data_test.plot(ax=ax, label='test')
predictions['pred'].plot(ax=ax, label='predictions')
ax.fill_between(
predictions.index,
predictions['lower_bound'],
predictions['upper_bound'],
alpha=0.5
)
ax.legend()
Backtesting
# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from skforecast.model_selection import backtesting_forecaster
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
# Download data
# ==============================================================================
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv')
data = pd.read_csv(url, sep=',', header=0, names=['y', 'datetime'])
# Data preprocessing
# ==============================================================================
data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d')
data = data.set_index('datetime')
data = data.asfreq('MS')
data = data['y']
data = data.sort_index()
# Split train-backtest
# ==============================================================================
n_backtest = 36*3 # Last 9 years are used for backtest
data_train = data[:-n_backtest]
data_backtest = data[-n_backtest:]
# Plot
# ==============================================================================
fig, ax=plt.subplots(figsize=(9, 4))
data_train.plot(ax=ax, label='train')
data_backtest.plot(ax=ax, label='backtest')
ax.legend()
# Backtest forecaster
# ==============================================================================
forecaster = ForecasterAutoreg(
regressor = RandomForestRegressor(random_state=123),
lags = 15
)
metric, predictions_backtest = backtesting_forecaster(
forecaster = forecaster,
y = data,
initial_train_size = len(data_train),
steps = 12,
metric = 'mean_squared_error',
refit = True,
verbose = True
)
Information of backtesting process
----------------------------------
Number of observations used for initial training: 96
Number of observations used for backtesting: 108
Number of folds: 9
Number of steps per fold: 12
Data partition in fold: 0
Training: 1991-07-01 00:00:00 -- 1999-06-01 00:00:00
Validation: 1999-07-01 00:00:00 -- 2000-06-01 00:00:00
Data partition in fold: 1
Training: 1991-07-01 00:00:00 -- 2000-06-01 00:00:00
Validation: 2000-07-01 00:00:00 -- 2001-06-01 00:00:00
Data partition in fold: 2
Training: 1991-07-01 00:00:00 -- 2001-06-01 00:00:00
Validation: 2001-07-01 00:00:00 -- 2002-06-01 00:00:00
Data partition in fold: 3
Training: 1991-07-01 00:00:00 -- 2002-06-01 00:00:00
Validation: 2002-07-01 00:00:00 -- 2003-06-01 00:00:00
Data partition in fold: 4
Training: 1991-07-01 00:00:00 -- 2003-06-01 00:00:00
Validation: 2003-07-01 00:00:00 -- 2004-06-01 00:00:00
Data partition in fold: 5
Training: 1991-07-01 00:00:00 -- 2004-06-01 00:00:00
Validation: 2004-07-01 00:00:00 -- 2005-06-01 00:00:00
Data partition in fold: 6
Training: 1991-07-01 00:00:00 -- 2005-06-01 00:00:00
Validation: 2005-07-01 00:00:00 -- 2006-06-01 00:00:00
Data partition in fold: 7
Training: 1991-07-01 00:00:00 -- 2006-06-01 00:00:00
Validation: 2006-07-01 00:00:00 -- 2007-06-01 00:00:00
Data partition in fold: 8
Training: 1991-07-01 00:00:00 -- 2007-06-01 00:00:00
Validation: 2007-07-01 00:00:00 -- 2008-06-01 00:00:00
fig, ax = plt.subplots(figsize=(9, 4))
data_backtest.plot(ax=ax, label='backtest')
predictions_backtest.plot(ax=ax, label='predictions')
ax.legend()
Model tuning
# Libraries
# ==============================================================================
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from skforecast.model_selection import grid_search_forecaster
from sklearn.ensemble import RandomForestRegressor
# Download data
# ==============================================================================
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv')
data = pd.read_csv(url, sep=',', header=0, names=['y', 'datetime'])
# Data preprocessing
# ==============================================================================
data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d')
data = data.set_index('datetime')
data = data.asfreq('MS')
data = data['y']
data = data.sort_index()
# Split train-test
# ==============================================================================
steps = 24
data_train = data.loc[: '2001-01-01']
data_val = data.loc['2001-01-01' : '2006-01-01']
data_test = data.loc['2006-01-01':]
# Plot
# ==============================================================================
fig, ax=plt.subplots(figsize=(9, 4))
data_train.plot(ax=ax, label='train')
data_val.plot(ax=ax, label='validation')
data_test.plot(ax=ax, label='test')
ax.legend()
# Grid search hyperparameters and lags
# ==============================================================================
forecaster = ForecasterAutoreg(
regressor = RandomForestRegressor(random_state=123),
lags = 12 # Placeholder, the value will be overwritten
)
# Regressor hyperparameters
param_grid = {'n_estimators': [50, 100],
'max_depth': [5, 10, 15]}
# Lags used as predictors
lags_grid = [3, 10, [1, 2, 3, 20]]
results_grid = grid_search_forecaster(
forecaster = forecaster,
y = data.loc[:'2006-01-01'],
param_grid = param_grid,
lags_grid = lags_grid,
steps = 12,
refit = True,
metric = 'mean_squared_error',
initial_train_size = len(data_train),
return_best = True,
verbose = False
)
results_grid
Number of models compared: 18
loop lags_grid: 0%| | 0/3 [00:00
| lags | params | metric | max_depth | n_estimators |
|---------------------------------|----------------------------------------|-----------|-------------|----------------|
| [ 1 2 3 4 5 6 7 8 9 10] | {'max_depth': 5, 'n_estimators': 50} | 0.0334486 | 5 | 50 |
| [ 1 2 3 4 5 6 7 8 9 10] | {'max_depth': 10, 'n_estimators': 50} | 0.0392212 | 10 | 50 |
| [ 1 2 3 4 5 6 7 8 9 10] | {'max_depth': 15, 'n_estimators': 100} | 0.0392658 | 15 | 100 |
| [ 1 2 3 4 5 6 7 8 9 10] | {'max_depth': 5, 'n_estimators': 100} | 0.0395258 | 5 | 100 |
| [ 1 2 3 4 5 6 7 8 9 10] | {'max_depth': 10, 'n_estimators': 100} | 0.0402408 | 10 | 100 |
| [ 1 2 3 4 5 6 7 8 9 10] | {'max_depth': 15, 'n_estimators': 50} | 0.0407645 | 15 | 50 |
| [ 1 2 3 20] | {'max_depth': 15, 'n_estimators': 100} | 0.0439092 | 15 | 100 |
| [ 1 2 3 20] | {'max_depth': 5, 'n_estimators': 100} | 0.0449923 | 5 | 100 |
| [ 1 2 3 20] | {'max_depth': 5, 'n_estimators': 50} | 0.0462237 | 5 | 50 |
| [1 2 3] | {'max_depth': 5, 'n_estimators': 50} | 0.0486662 | 5 | 50 |
| [ 1 2 3 20] | {'max_depth': 10, 'n_estimators': 100} | 0.0489914 | 10 | 100 |
| [ 1 2 3 20] | {'max_depth': 10, 'n_estimators': 50} | 0.0501932 | 10 | 50 |
| [1 2 3] | {'max_depth': 15, 'n_estimators': 100} | 0.0505563 | 15 | 100 |
| [ 1 2 3 20] | {'max_depth': 15, 'n_estimators': 50} | 0.0512172 | 15 | 50 |
| [1 2 3] | {'max_depth': 5, 'n_estimators': 100} | 0.0531229 | 5 | 100 |
| [1 2 3] | {'max_depth': 15, 'n_estimators': 50} | 0.0602604 | 15 | 50 |
| [1 2 3] | {'max_depth': 10, 'n_estimators': 50} | 0.0609513 | 10 | 50 |
| [1 2 3] | {'max_depth': 10, 'n_estimators': 100} | 0.0673343 | 10 | 100 |
Using forecaster in production
A trained model may be deployed in production in order to generate predictions regularly. Suppose predictions have to be generated on a weekly basis, for example, every Monday. By default, when using the predict method on a trained forecaster object, predictions start right after the last training observation. Therefore, the model could be retrained weekly, just before the first prediction is needed, and call the predict method. This strategy, although simple, may not be possible to use for several reasons:
-
Model training is very expensive and cannot be run as often.
-
The history with which the model was trained is no longer available.
-
The prediction frequency is so high that there is no time to train the model between predictions.
In these scenarios, the model must be able to predict at any time, even if it has not been recently trained.
Every model generated using skforecast has the last_window argument in its predict method. Using this argument, it is possible to provide only the past values needs to create the autoregressive predictors (lags) and thus, generate the predictions without the need to retrain the model.
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from skforecast.ForecasterAutoreg import ForecasterAutoreg
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv')
data = pd.read_csv(url, sep=',', header=0, names=['y', 'date'])
data['date'] = pd.to_datetime(data['date'], format='%Y/%m/%d')
data = data.set_index('date')
data = data.asfreq('MS')
data_train = data.loc[:'2005-01-01']
data_train.tail()
date
y
2004-09-01 00:00:00
1.13443
2004-10-01 00:00:00
1.18101
2004-11-01 00:00:00
1.21604
2004-12-01 00:00:00
1.25724
2005-01-01 00:00:00
1.17069
forecaster = ForecasterAutoreg(
regressor = RandomForestRegressor(),
lags = 5
)
forecaster.fit(y=data_train['y'])
2005-02-01 0.927480
2005-03-01 0.756215
2005-04-01 0.692595
Freq: MS, Name: pred, dtype: float64
As expected, predictions follow directly from the end of training data. When last window is provided, the forecaster uses this data to generate the lags needed as predictors and starts the prediction afterwards.
forecaster.predict(steps=3, last_window=data['y'].tail(5))
2008-07-01 0.803853
2008-08-01 0.870858
2008-09-01 0.905003
Freq: MS, Name: pred, dtype: float64
Since the provided last_window contains values from 2008-02-01 to 2008-06-01, the forecaster is able to create the needed lags and predict the next 5 steps.
⚠
WARNING:
It is important to note that the length of last windows must be enough to include the maximum lag used by the forecaster. Fore example, if the forecaster uses lags 1, 24, 48 and 72 last_window must include the last 72 values of the series.
Examples and tutorials
English
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Skforecast: time series forecasting with Python and Scikit-learn
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Forecasting time series with gradient boosting: Skforecast, XGBoost, LightGBM and CatBoost
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Español
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Skforecast: forecasting series temporales con Python y Scikit-learn
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Forecasting series temporales con gradient boosting: Skforecast, XGBoost, LightGBM y CatBoost
Donating
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Licence
joaquinAmatRodrigo/skforecast is licensed under the MIT License, a short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
![Backtesting with overlap in the validation sets [parameter defining forecast origin shift]](https://avatars.githubusercontent.com/u/32712708?v=4)
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