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The purpose of this paper is to give an overview of the time seriesforecasting problem based on similarity of trajectories. Various methodologiesare introduced and studied, and detailed discussions on hyperparameteroptimization, outlier handling and distance measures are provided. Thesuggested new approaches involve variations in both the selection of similartrajectories and assembling the candidate forecasts. After forming a generalframework, an experimental study is conducted to compare the methods that usesimilar trajectories along with some other standard models (such as ARIMA andRandom Forest) from the literature. Lastly, the forecasting setting is extendedto interval forecasts, and the prediction intervals resulting from the similartrajectories approach are compared with the existing models from theliterature, such as historical simulation and quantile regression. Throughoutthe paper, the experimentations and comparisons are conducted via the timeseries of traffic flow from the California PEMS dataset.

An overview of time series point and interval forecasting based on  similarity of trajectories, with an experimental study on traffic flow  forecasting