Referential kNN Regression for Financial Time Series Forecasting

Tao Ban, Ruibin Zhang, Shaoning Pang, Abdolhossein Sarrafzadeh, Daisuke Inoue

Abstract: In this paper we propose a new multivariate regression approach for financial time series forecasting based on knowledge shared from referential nearest neighbors. Our approach defines a two-tier architecture. In the top tier, the nearest neighbors that bear referential information for a target time series are identified by exploiting the financial correlation from the historical data. Next, the future status of the target financial time series is inferred from heritage of the time series by using a multivariate k-Nearest-Neighbour (kNN) regression model exploiting the aggregated knowledge from all relevant referential nearest neighbors. The performance of the proposed multivariate kNN approach is assessed by empirical evaluation on the 9-year S&P 500 stock data. The experimental results show that the proposed approach provides enhanced forecasting accuracy than the referred univariate kNN regression.