Joint prediction of time series data in inventory management

The problem of time series prediction has been well explored in the community of data mining. However, little research attention has been paid to the case of predicting the movement of a collection of related time series data. In this work, we study the problem of simultaneously predicting multiple time series data using joint predictive models. We observe that in real-world applications, strong relationships between different time-sensitive variables are often held, either explicitly predefined or implicitly covered in nature of the application. Such relationships indicate that the prediction on the trajectory of one given time series could be improved by incorporating the properties of other related time series data into predictive models. The key challenge is to capture the temporal dynamics of these relationships to jointly predict multiple time series. In this research, we propose a predictive model for multiple time series forecasting and apply it to the domain of inventory management. The relationships among multiple time series are modeled as a class of constraints, and in turn, refine the predictions on the corresponding time series. Experimental results on real-world data reveal that the proposed algorithms outperform well-established methods of time series forecasting.