1 research outputs found
An overview of time series point and interval forecasting based on similarity of trajectories, with an experimental study on traffic flow forecasting
The purpose of this paper is to give an overview of the time series
forecasting problem based on similarity of trajectories. Various methodologies
are introduced and studied, and detailed discussions on hyperparameter
optimization, outlier handling and distance measures are provided. The
suggested new approaches involve variations in both the selection of similar
trajectories and assembling the candidate forecasts. After forming a general
framework, an experimental study is conducted to compare the methods that use
similar trajectories along with some other standard models (such as ARIMA and
Random Forest) from the literature. Lastly, the forecasting setting is extended
to interval forecasts, and the prediction intervals resulting from the similar
trajectories approach are compared with the existing models from the
literature, such as historical simulation and quantile regression. Throughout
the paper, the experimentations and comparisons are conducted via the time
series of traffic flow from the California PEMS dataset.Comment: 32 page