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

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