web_traffic_train

<p>A dataframe containing 120 instances that can be used to train the SETAR-Tree and SETAR-Forest models.
The data are related to the number of hits or web traffic of a set of Wikipedia pages.
Each instance in the dataframe consists of 10 time series lags (Lag1 to Lag10), a categorical covariate (Project) and the corresponding true outputs (y).
The data were downloaded from the Wikimedia REST API (Wikimedia, 2022).</p>

datasets

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.

web_traffic_train: A dataframe of training instances

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Examples