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

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

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 e...

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Main Authors: Hongyan Jiang, Dianjun Fang, Klaus Spicher, Feng Cheng, Boxing Li
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Applied Sciences
Subjects:
forecasting
time series data
period-sequential index algorithm
neural networks
Online Access:https://www.mdpi.com/2076-3417/9/20/4386
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spelling doaj-0143547b7a214210ab036080c9ce7ed82020-11-25T01:32:43ZengMDPI AGApplied Sciences2076-34172019-10-01920438610.3390/app9204386app9204386A New Period-Sequential Index Forecasting Algorithm for Time Series DataHongyan Jiang0Dianjun Fang1Klaus Spicher2Feng Cheng3Boxing Li4School of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaSchool of Mechanical Engineering, Jiangnan University, Wuxi 214122, ChinaSchool of Mechanical Engineering, Tongji University, Shanghai 201804, ChinaA 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.https://www.mdpi.com/2076-3417/9/20/4386forecastingtime series dataperiod-sequential index algorithmneural networks
collection DOAJ
language English
format Article
sources DOAJ
author Hongyan Jiang
Dianjun Fang
Klaus Spicher
Feng Cheng
Boxing Li
spellingShingle Hongyan Jiang
Dianjun Fang
Klaus Spicher
Feng Cheng
Boxing Li
A New Period-Sequential Index Forecasting Algorithm for Time Series Data
Applied Sciences
forecasting
time series data
period-sequential index algorithm
neural networks
author_facet Hongyan Jiang
Dianjun Fang
Klaus Spicher
Feng Cheng
Boxing Li
author_sort Hongyan Jiang
title A New Period-Sequential Index Forecasting Algorithm for Time Series Data
title_short A New Period-Sequential Index Forecasting Algorithm for Time Series Data
title_full A New Period-Sequential Index Forecasting Algorithm for Time Series Data
title_fullStr A New Period-Sequential Index Forecasting Algorithm for Time Series Data
title_full_unstemmed A New Period-Sequential Index Forecasting Algorithm for Time Series Data
title_sort new period-sequential index forecasting algorithm for time series data
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2019-10-01
description 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.
topic forecasting
time series data
period-sequential index algorithm
neural networks
url https://www.mdpi.com/2076-3417/9/20/4386
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