Data analysis and processing for spatio-temporal forecasting

Abstract

Spatio-temporal forecasting is a research area applicable to many industrial fields, such as forecasting power consumption in real-life and predicting traffic conditions of roads. For example, in the traffic forecasting, it is important to analyze spatial relations and temporal trends in order to predict traffic changes in roads over time. In the spatio-temporal forecasting task, previous studies applied graph modeling to capture spatial relations. However, existing models use only the recently available data to predict traffic conditions, leading to the degraded performance of the model. Further research is necessary for predicting the speed in the far future. As a study to tackle this issue, we aim to improve the performance of the model by providing the model with additional data through time-series segmentation. In order to verify whether the additional data could be meaningful to the model, an experiment was conducted to compare the performance of the model trained with existing data and the model trained with our data and analyze the distribution of the additional data.