Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm

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Surakhi, O.; Zaidan, M.A.; Fung, P.L.; Hossein Motlagh, N.; Serhan, S.; AlKhanafseh, M.; Ghoniem, R.M.; Hussein, T. Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm. Electronics 2021, 10, 2518.

Title: Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm
Author: Surakhi, Ola; Zaidan, Martha A.; Fung, Pak Lun; Hossein Motlagh, Naser; Serhan, Sami; AlKhanafseh, Mohammad; Ghoniem, Rania M.; Hussein, Tareq
Publisher: Multidisciplinary Digital Publishing Institute
Date: 2021-10-15
URI: http://hdl.handle.net/10138/335600
Abstract: 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.


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