Abstract
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 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 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.
Highlights
In the big data era, a large number of time series data are continuously generated in the network systems, such as stock price, sales volume, production capacity, weather data, ocean engineering, engineering control, and largely in any system of applied science and engineering which involves investigations of time-varying parameters [1,2,3]
In order to evaluate the obtained results, the forecasting accuracy was measured with three error indicators, that are the mean absolute percentage error (MAPE), the root mean squared error (RMSE)
A comparison analysis of the prediction value and real was was implemented by using the sigma-pi neural network algorithm (SPNN)-period-sequential index algorithm (PSI), moving average (MA),MA, exponential smoothing (ES), ES, andand methods, so as a the value real value implemented by using the SPNN-PSI, autoregressive integrated moving average (ARIMA)
Summary
In the big data era, a large number of time series data are continuously generated in the network systems, such as stock price, sales volume, production capacity, weather data, ocean engineering, engineering control, and largely in any system of applied science and engineering which involves investigations of time-varying parameters [1,2,3]. Adaptive models [14] have been used to forecast nonlinear time series data, and improve forecasting accuracy in different time scales. It is possible to hybrid different methods to improve overall forecasting accuracy [15]. It is possible to hybrid different methods toHowever, improve overall forecasting accuracy [15]. Sigma-pi neural network algorithm (SPNN) improving the accuracy and robustness of forecasting. SPNN-PSI hasapplication, a universal application, and a satisfactory prediction quality as theimproved correlation coefficient value of the reference historical datasets increased. As the correlation coefficient value of the reference historical datasets increased
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