Forecasting Using Point-valued Time Series and Fuzzy-valued Time Series Models

Abstract

The point-valued time series (PTS) is simply about one value in each time or period of the data, but when the data have two values at each time, the suitable time series is called the interval-valued time series (ITS). An example of ITS is the daily close and open stock prices. If the time series data are of typed linguistic data, for example, “low increase”, “medium increase” and “high increase”, the time series data are called a fuzzy-valued time series or being well-known as fuzzy time series (FTS). The aim of this study is to compare the PTS and FTS models for forecasting stock prices in the stock market. The stock market is one of the essential investments in the economy. The movement of stock price can be leading or declining which respectively expands or contracts a country’s economy. The movement may also represent the scenario or event happening in a company. Therefore, forecasting stock price movement is highly important since it will assist investors and sellers to make planning in their investment’s decision. Therefore, the objectives of this study are to identify the best forecasting models in point-valued time series (PTS) and fuzzy time series (FTS) based on forecasting measurement error. Eight stock price forecasting models consisting of four models from PTS and four models from FTS. Meanwhile, four forecasting measurement errors were discussed in the analysis as a criterion for choosing the best forecasting model. A set of daily historical data from the Bursa Malaysia website was used as a basis for analysis. The finding shows the simple exponential smoothing model is the best PTS model. In the meantime, the Cheng model based on Sturges’ rule is the best FTS model. However, among these two types of models, the Cheng model is found to be the best model with forecasting measurement errors of 0.0001 (MSFE), 0.0108 (RMSFE) and 1.1918 (MAPFE). The results reveal that besides the PTS model, the FTS model is an alternative model to forecast stock market movement. Moreover, these FTS and PTS models can also be applied to solving other forecasting problems.