A New Period-Sequential Index Forecasting Algorithm for Time Series Data

A New Period-Sequential Index Forecasting Algorithm for Time Series Data

Blog Article

A period-sequential index algorithm with sigma-pi neural network technology, which is called the (SPNN-PSI) method, is proposed for the prediction of time series datasets.Using the SPNN-PSI method, the cumulative electricity output (CEO) dataset, Volkswagen sales (VS) dataset, and electric motors exports (EME) dataset are tested.The results show that, in contrast to the Vitamin D3 + K2 moving average (MA), exponential smoothing (ES), and autoregressive integrated moving average (ARIMA) methods, the proposed SPNN-PSI method shows satisfactory forecasting quality due to lower error, and is more suitable for the prediction of time series datasets.It is also concluded that: There is a trend that the higher the correlation coefficient value of the SOAP reference historical datasets, the higher the prediction quality of SPNN-PSI method, and a higher value (>0.

4) of correlation coefficient for SPNN-PSI method can help to improve occurrence probability of higher forecasting accuracy, and produce more accurate forecasts for the big datasets.