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

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.

An Adaptive Control Combination Forecasting Method for Time Series Data

“…(1) Period-Sequential Index Method e Period-Sequential Index (PSI) method [6] had been studied by the author in the previous work. e PSI model introduced the period index (PI) and sequential index (SI) to describe the dataset structure information in vertical and horizontal dimensions, respectively.…”

“…e more detailed derivation can be found in Ref. [6]. (2) Exponential Smoothing Method e Exponential Smoothing (ES) method [9] is often used in practice to forecast time series.…”

“…In physical methods, effective forecasting results rely on physical information [4,5], but it was not proficient in dealing with short-term series with complex calculation process. Statistical models, including Period-Sequential Index (PSI) [6], moving average (MA) [7], autoregressive integrated moving average (ARIMA) [8], exponential smoothing [9], Kalman filter [10], and grey forecasting [11], effectively tackled linear features but gave larger error for a fluctuant, seasonal one [12], noise, or instability [13]. Artificial intelligence models, subsuming BP neural network (BP-NET) [14,15], support vector machines (SVM) [16], fuzzy logic models [17], and least square support vector machine (LSSVM) [18], have exhibited significant advantages in dealing with nonlinear problems.…”

Comparative Study on Exponentially Weighted Moving Average Approaches for the Self-Starting Forecasting

“…This exists in many situations, including the daily closing value of the stock market, manufacturing process, health status of patients, and economic indicators [1]. Using these time series data, forecasting future events has been of considerable interest in various fields, including control charts [2][3][4][5], health care surveillance [6][7][8], inventory controls [9], stock market prediction [10][11][12], pandemic occurrence prediction [13], and electricity demand forecasting [14]. In such industrial areas, the accurate forecasting of future time series values helps establish more effective decision or management policy.…”