A novel forecasting method based on multi-order fuzzy time series and technical analysis - Ye, Furong; Zhang, Liming; Zhang, Defu; Fujita, Hamido; Gong, Zhiguo (Information Sciences, 2016)
•The directional accuracy rate of forecasting model is first selected as performance measures.•Exponential smoothing is used to eliminate noise in the time series.•Multi-order and multivariate fuzzy time series are combined to forecast financial time series.•The proposed method outperforms the existing methods for 5 well-known financial time series. Financial trading is one of the most common risk investment actions in the modern economic environment because financial market systems are complex non-linear dynamic systems. It is a challenge to develop the inherent rules using the traditional time series prediction technique. In this paper, we proposed a new forecasting method based on multi-order fuzzy time series, technical analysis, and a genetic algorithm. Multi-order fuzzy time series (first-order, second-order and third-order) are applied in the proposed algorithm, and to improve the performance, genetic algorithm is used to find a good domain partition. Technical analysis such as the Rate of Change (ROC), Moving Average Convergence/Divergence (MACD), and Stochastic Oscillator (KDJ) are introduced to construct multi-variable fuzzy time series, and exponential smoothing is used to eliminate noise in the time series. In addition to the root mean square error and mean square error, the directional accuracy rate (DAR) is also used in our empirical studies. We apply the proposed method to forecast five well-known stock indexes and the NTD/USD exchange rates. Experimental results demonstrate that our proposed method outperforms other existing models based on fuzzy time series.