tspiral 0.1.5

A python package for time series forecasting with scikit-learn estimators.

tspiral

A python package for time series forecasting with scikit-learn estimators.

tspiral is not a library that works as a wrapper for other tools and methods for time series forecasting. tspiral directly provides scikit-learn estimators for time series forecasting. It leverages the benefit of using scikit-learn syntax and components to easily access the open source ecosystem built on top of the scikit-learn community. It easily maps a complex time series forecasting problems into a tabular supervised regression task, solving it with a standard approach.

Overview

tspiral provides 4 optimized forecasting techniques:

• Recursive Forecasting

Lagged target features are combined with exogenous regressors (if provided) and lagged exogenous features (if specified). A scikit-learn compatible regressor is fitted on the whole merged data. The fitted estimator is called iteratively to predict multiple steps ahead.

Which in a compact way we can summarize in:

• Direct Forecasting

A scikit-learn compatible regressor is fitted on the lagged data for each time step to forecast.

• Stacking Forecasting

Multiple recursive time series forecasters are fitted and combined on the final portion of the training data with a meta-learner.

• Rectified Forecasting

Multiple recursive time series forecasters are fitted on different sliding window training bunches. Forecasts are adjusted and combined fitting a meta-learner for each forecasting step.

Multivariate time series forecasting is natively supported for all the forecasting methods available.

Installation

pip install --upgrade tspiral

The module depends only on NumPy and Scikit-Learn (>=0.24.2). Python 3.6 or above is supported.

Media

• How to Improve Recursive Time Series Forecasting

Usage

• Recursive Forecasting

import numpy as np
from sklearn.linear_model import Ridge
from tsprial.forecasting import ForecastingCascade
timesteps = 400
e = np.random.normal(0,1, (timesteps,))
y = 2*np.sin(np.arange(timesteps)*(2*np.pi/24))+e
model = ForecastingCascade(
    Ridge(),
    lags=range(1,24+1),
    use_exog=False,
    accept_nan=False
)
model.fit(None, y)
forecasts = model.predict(np.arange(24*3))

• Direct Forecasting

import numpy as np
from sklearn.linear_model import Ridge
from tsprial.forecasting import ForecastingChain
timesteps = 400
e = np.random.normal(0,1, (timesteps,))
y = 2*np.sin(np.arange(timesteps)*(2*np.pi/24))+e
model = ForecastingChain(
    Ridge(),
    n_estimators=24,
    lags=range(1,24+1),
    use_exog=False,
    accept_nan=False
)
model.fit(None, y)
forecasts = model.predict(np.arange(24*3))

• Stacking Forecasting

import numpy as np
from sklearn.linear_model import Ridge
from sklearn.tree import DecisionTreeRegressor
from tsprial.forecasting import ForecastingStacked
timesteps = 400
e = np.random.normal(0,1, (timesteps,))
y = 2*np.sin(np.arange(timesteps)*(2*np.pi/24))+e
model = ForecastingStacked(
    [Ridge(), DecisionTreeRegressor()],
    test_size=24*3,
    lags=range(1,24+1),
    use_exog=False
)
model.fit(None, y)
forecasts = model.predict(np.arange(24*3))

• Rectified Forecasting

import numpy as np
from sklearn.linear_model import Ridge
from tsprial.forecasting import ForecastingRectified
timesteps = 400
e = np.random.normal(0,1, (timesteps,))
y = 2*np.sin(np.arange(timesteps)*(2*np.pi/24))+e
model = ForecastingRectified(
    Ridge(),
    n_estimators=200,
    test_size=24*3,
    lags=range(1,24+1),
    use_exog=False
)
model.fit(None, y)
forecasts = model.predict(np.arange(24*3))

More examples in the notebooks folder.