Time Series Cross-Sequence Prediction

In the modern transport industry, vast and diverse information arrays, particularly those including time series data, are rapidly expanding. This growth presents an opportunity to improve the quality of forecasting. Researchers and practitioners are continuously developing innovative tools to predict their future values. The goal of the research is to improve the performance of automated forecasting environments in a systematic and structured way. This paper investigates the effect of substituting the initial time series with another of a similar nature, during the training phase of the model’s development. A financial data set and the Prophet model are employed for this objective. It is observed that the impact on the accuracy of the predicted future values is promising, albeit not significant. Based on the obtained results, valuable conclusions are drawn, and recommendations for further improvements are provided. By highlighting the importance of diverse data incorporation, this research assists in making informed choices and leveraging the full potential of available information for more precise forecasting outcomes.