Although time-series analysis techniques are commonly used in forecasting, most of them are only applicable to numerical and/or categorical data. This paper proposes a multi-type learning approach to improve their prediction accuracy. It is achieved by considering the knowledge from multiple distinct data types together, e.g., numbers and texts. Using this approach, we successfully boosted the prediction accuracy of a proprietary trade recommendation platform. Financial data are investigated due to the availability of their multi-type time-series and their challenging behaviors, such as high volatility. Different from traditional sentiment analysis and topic modeling methods, market-sentiment linked topics are discovered by our proposed models to quantify market conditions. The topics are learned from historical multi-type time-series instead of using a common perception of term polarities. By capturing domain specific knowledge, an impressive 36% improvement in portfolio return is obtained as shown in experiments.
['Victor W. Chu', 'Raymond K. Wong', 'Fang Chen', 'Ivan Ho', 'Joe Lee']
Market-sentiment boosted predictions on multi-type time-series