An improved model of fuzzy time series based on fibonacci sequence for klci stock index forecasting

Abstract

Time series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather and many more. For promoting the forecasting performance of fuzzy time-series models, Tai-Liang Chen, Ching-Hsue Cheng and Hia-Jong Teoh had proposed a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. Their findings were shown in the journal entitled "Fuzzy time-series based on Fibonacci sequence for stock price forecasting (2007)". We noticed that length of intervals somehow affects the performance of fuzzy time series as proposed by Huamg (2000) who argued that different lengths of intervals lead to different forecasting results and forecasting errors. Consequently, it affects the performance of the model proposed by Chen, T.L., Cheng and Teoh (2007). Therefore, we employ the frequency-density-based partitioning into their model in order to compare it with its original randomly chosen length of intervals partitioning. This paper employs a 2-year weekly period of Kuala Lumpur Composite Index (KLCI) stock index data as experimental datasets. Through comparison of the forecasting performances of our model with their model, we noticed that our model has smaller forecasting error. Hence, conclude that our model is an improved model of the model proposed by Chen, T.L., Cheng and Teoh (2007) previously.