Forecasting

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.

Introduction

Forecasating is making forward temporal predictions based on past data. The simplest case is the univariate case.

Inputs.

  • Univariate time series, \(y = y(0), y(1), y(2), ..., y(N)\)

  • Forecasting horizon, \(fh = N+1, N+2, ..., N+h\)

Output.

  • Predictions of \(y\) at the times in \(fh\), \(\hat{y} = \hat{y}(N+1), \hat{y}(N+2), ..., \hat{y}(N+h)\)

Examples.

  • Forecasting the global population

  • Forecasting the price of a stock

  • Forecasting the daily maximum temperature in a given location

Example

Predicting flights. See the Tutorial on Forecasting.

Algorithms included in sktime

See the API Reference.

Reductions included in sktime

Variations in generative setting

Evaluation and model selection

Algorithms not included in sktime

Further reading