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Time-Lag Selection for Time-Series Forecasting Using Neural Network and Heuristic Algorithm
- Source :
- Electronics, Vol 10, Iss 2518, p 2518 (2021), Electronics, Volume 10, Issue 20
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
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.
- Subjects :
- Artificial Neural Network
Mean squared error
TK7800-8360
Computer Networks and Communications
Computer science
MODELS
air pollution
Recurrent Neural Network
PREDICTIONS
02 engineering and technology
0202 electrical engineering, electronic engineering, information engineering
heuristic algorithm
Electrical and Electronic Engineering
Time series
Artificial neural network
SHORT-TERM-MEMORY
business.industry
Deep learning
Autocorrelation
deep learning
020206 networking & telecommunications
Function (mathematics)
113 Computer and information sciences
time-series forecasting
Mean absolute percentage error
Recurrent neural network
13. Climate action
Hardware and Architecture
Control and Systems Engineering
Signal Processing
020201 artificial intelligence & image processing
Artificial intelligence
Electronics
business
LSTM
Algorithm
Subjects
Details
- Language :
- English
- ISSN :
- 20799292
- Volume :
- 10
- Issue :
- 2518
- Database :
- OpenAIRE
- Journal :
- Electronics
- Accession number :
- edsair.doi.dedup.....749c089b6c86bbb51a0e078841a775d3