CRAN/E | setartree

setartree

SETAR-Tree - A Novel and Accurate Tree Algorithm for Global Time Series Forecasting

Installation

About

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) . 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.

github.com/rakshitha123/setartree
Bug report File report

Key Metrics

Version 0.2.1
R ≥ 3.5.0
Published 2023-08-24 407 days ago
Needs compilation? no
License MIT
License File
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Maintainer

Maintainer

Rakshitha Godahewa

Authors

Rakshitha Godahewa

cre / aut / cph

Christoph Bergmeir

aut

Daniel Schmidt

aut

Geoffrey Webb

ctb

Material

ChangeLog
Reference manual
Package source

In Views

TimeSeries

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

setartree archive

Depends

R ≥ 3.5.0

Imports

stats
utils
methods
parallel
generics ≥ 0.1.2

Suggests

forecast