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Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm
Content Provider | MDPI |
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Author | Surakhi, Ola Zaidan, Martha A. Fung, Pak Lun Motlagh, Naser Hossein Serhan, Sami AlKhanafseh, Mohammad Ghoniem, Rania M. Hussein, Tareq |
Copyright Year | 2021 |
Description | The time-series forecasting is a vital area that motivates continuous investigate areas of intrigued for different applications. A critical step for the time-series forecasting is the right determination of the number of past observations (lags). This paper investigates the forecasting accuracy based on the selection of an appropriate time-lag value by applying a comparative study between three methods. These methods include a statistical approach using auto correlation function, a well-known machine learning technique namely Long Short-Term Memory (LSTM) along with a heuristic algorithm to optimize the choosing of time-lag value, and a parallel implementation of LSTM that dynamically choose the best prediction based on the optimal time-lag value. The methods were applied to an experimental data set, which consists of five meteorological parameters and aerosol particle number concentration. The performance metrics were: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared. The investigation demonstrated that the proposed LSTM model with heuristic algorithm is the superior method in identifying the best time-lag value. |
Starting Page | 2518 |
e-ISSN | 20799292 |
DOI | 10.3390/electronics10202518 |
Journal | Electronics |
Issue Number | 20 |
Volume Number | 10 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-10-15 |
Access Restriction | Open |
Subject Keyword | Electronics Industrial Engineering Air Pollution Artificial Neural Network Deep Learning Heuristic Algorithm Recurrent Neural Network Time-series Forecasting |
Content Type | Text |
Resource Type | Article |