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.

翻译：本文旨在综述基于轨迹相似性的时间序列预测问题。文中介绍并研究了多种方法，对超参数优化、异常值处理及距离度量进行了详细讨论。所提出的新方法涉及相似轨迹选取与候选预测组合两方面的变化。在构建通用框架后，本文通过实验研究，将基于相似轨迹的方法与文献中其他标准模型（如ARIMA和随机森林）进行了比较。最后，将预测框架扩展至区间预测，并将基于相似轨迹方法得到的预测区间与文献中现有模型（如历史模拟和分位数回归）进行了对比。全文中，实验与比较均基于加利福尼亚州PEMS数据集的交通流量时间序列进行。