Fuzzy based trend mapping and forecasting for time series data

The study demonstrates the superiority of fuzzy based methods for non-stationary, non-linear time series. Study is based on unequal length fuzzy sets and uses IF-THEN based fuzzy rules to capture the trend prevailing in the series. The proposed model not only predicts the value but can also identify the transition points where the series may change its shape and is ready to include subject expert’s opinion to forecast. The series is tested on three different types of data: enrolment for Alabama university, sales volume of a chemical company and Gross domestic capital of India: the growth curve. The model is tested on both kind of series: with and without outliers. The proposed model provides an improved prediction with lesser MAPE (mean average percentage error) for all the series tested.

► Proposed method can notify the change in trend as a little high, low or moderate. ► Can predict when the next value may change the shape ‘‘downward or upward”. ► Can foresee transitional points and incorporate subjective expert’s opinion. ► Works with outliers too so the universe of discourse can incorporate even the rare possibilities. ► Tested on short series with and without outliers, financial time series and gave improved result.