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.
Since 1970s, many academic researchers and business practitioners have started to develop different forecasting methods and models. Most of them are still used in the IT-Systems nowadays. However, they don't perform well enough in practice. People pay much attention to data collection but ignore the data quality, which could lead to low forecasting accuracy. In this paper, we will introduce two new heuristic business forecasting techniques (Revinda and Metrix). Both methods utilize inherent structures of time series. The error analysis is based on B2C and B2B aggregated commercial data. In addition, these two methods will be compared with HoLT-WiNTERS-Methods (HWM) by using error measures MAPE, percentage better and Theil's U2
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