Learning to Rank for Multi-Step Ahead Time-Series Forecasting

Time-series forecasting is a fundamental problem associated with a wide range of engineering, financial, and social applications. The challenge arises from the complexity due to the time-variant property of time series and the inevitable diminishing utility of predictive models. Therefore, it is generally difficult to accurately predict values, especially in a multi-step ahead setting. However, in domains such as financial time series forecasting, an ex-ante prediction of the relative order of values in the near future is sufficient; i.e., the next 100 days can help make meaningful investment decisions. In this paper, we propose a dynamic prediction framework that makes it possible to make an ex-ante forecast of time series with a special focus on the relative ordering of the forecast within a forward-looking time horizon. Through the lens of the concordance index (CI), we compare the proposed method with conventional regression-based time-series forecasting methods, discriminative learning methods and hybrid methods. Moreover, we discuss the use of the proposed framework for different types of time series and under a variety of conditions. Extensive experimental results on financial time series across a majority of liquid asset classes show that the proposed framework outperforms the benchmark methods significantly.