Introduction

Note

The user guide is under development. We have created a basic structure and are looking for contributions to develop the user guide further. For more details, please go to issue #361 on GitHub.

sktime is a unified toolbox for machine learning with time series. Time series give rise to multiple learning tasks (e.g. forecasting and time series classification). The goal of sktime is to provide all the necessary tools to solve these tasks, including dedicated time series algorithms as well as tools for building, tuning and evaluating composite models.

Many of these tasks are related. An algorithm that can solve one of them can often be re-used to help solve another one, an idea called reduction. sktime’s unified interface allows to easily adapt an algorithm for one task to another.

For example, to use a regression algorithm to solve a forecasting task, we can simply write:

import numpy as np
from sktime.datasets import load_airline
from sktime.forecasting.compose import make_reduction
from sklearn.ensemble import RandomForestRegressor
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError

y = load_airline()
y_train, y_test = temporal_train_test_split(y)
fh = np.arange(1, len(y_test) + 1)  # forecasting horizon
regressor = RandomForestRegressor()
forecaster = make_reduction(
    regressor,
    strategy="recursive",
    window_length=12,
    scitype="infer",
)
forecaster.fit(y_train)
y_pred = forecaster.predict(fh)
smape = MeanAbsolutePercentageError()
smape(y_test, y_pred)
>>> 0.1261192310833735

For more details, check out our paper.

Currently, sktime provides:

  • State-of-the-art algorithms for time series classification and regression, ported from the Java-based tsml toolkit, as well as forecasting,

  • Transformers, including single-series transformations (e.g. detrending or deseasonalization) and series-as-features transformations (e.g. feature extractors), as well as tools to compose different transformers,

  • Pipelining,

  • Tuning,

  • Ensembling, such as a fully customisable random forest for time-series classification and regression, as well as ensembling for multivariate problems,

For a list of implemented methods, see our estimator overview.

In addition, sktime includes an experimental high-level API that unifies multiple learning tasks, partially inspired by the APIs of mlr and openML.