A multi-period-sequential-index combination method for short-term prediction of small sample data

Abstract

Based on multi-period-sequential-index combination (MPSIC) technology, three forecasting methods (auto-MPSIC, IV- MPSIC, MSEI-MPSIC) were proposed for short-term prediction of small sample data. Natural gas datasets, coal datasets, electricity datasets and atmosphere datasets were separately tested by using MPSIC method, and then impact of weighting factors, forecasting accuracy analysis were carried out for MPSIC method as well as other comparative methods. The results showed that, auto-MPSIC method was partial to use statistical indicators, such as peak-to-peak, average, root mean square, to decrease prediction error, and meanwhile was also inclined to use sequential index at time of ti-1 next to ti to improve prediction accuracy. It was also concluded that: the proposed MPSIC method could achieve higher prediction accuracy compared with other methods; the robustness of auto-MPSIC method was slightly better than that of IV-MPSIC and MSEI-MPSIC under condition of noisy data, which was attributed to an adaptive weight allocation technology considering statistical distribution of forecasting errors.