Package 'setartree'
| Title: |
SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time
Series Forecasting |
| Description: |
The implementation of a forecasting-specific tree-based
model that is in particular suitable for global time series
forecasting, as proposed in Godahewa et al. (2022)
<arXiv:2211.08661v1>. The model uses the concept of Self
Exciting Threshold Autoregressive (SETAR) models to define the
node splits and thus, the model is named SETAR-Tree. The
SETAR-Tree uses some time-series-specific splitting and
stopping procedures. It trains global pooled regression models
in the leaves allowing the models to learn cross-series
information. The depth of the tree is controlled by conducting
a statistical linearity test as well as measuring the error
reduction percentage at each node split. Thus, the SETAR-Tree
requires minimal external hyperparameter tuning and provides
competitive results under its default configuration. A forest
is developed by extending the SETAR-Tree. The SETAR-Forest
combines the forecasts provided by a collection of diverse
SETAR-Trees during the forecasting process. |
| Authors: |
Rakshitha Godahewa [cre, aut, cph],
Christoph Bergmeir [aut],
Daniel Schmidt [aut],
Geoffrey Webb [ctb] |
| Maintainer: |
Rakshitha Godahewa <[email protected]> |
| License: |
MIT + file LICENSE |
| Version: |
0.2.1 |
| Built: |
2024-11-16 03:49:19 UTC |
| Source: |
https://github.com/rakshitha123/setartree |
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