Long-Term Prediction of Small Time-Series Data Using Generalized Distillation

The recent increase of "big data" in our society has led to major impacts of machine learning and data mining technologies in various fields ranging from marketing to science. On the other hand, there still exist areas where only small-sized data are available for various reasons, for example, high data acquisition costs or the rarity of targets events. Machine learning tasks using such small data are usually difficult because of the lack of information available for training accurate prediction models. In particular, for long-term time-series prediction, the data size tends to be small because of the unavailability of the data between input and output times in training. Such limitations on the size of time-series data further make long-term prediction tasks quite difficult; in addition, the difficulty that the far future is more uncertain than the near future. In this paper, we propose a novel method for long-term prediction of small time-series data designed in the framework of generalized distillation. The key idea of the proposed method is to utilize the middle-time data between the input and output times as "privileged information," which is available only in the training phase and not in the test phase. We demonstrate the effectiveness of the proposed method on both synthetic data and real-world data. The experimental results show the proposed method performs well, particularly when the task is difficult and has high input dimensions.