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Effect of Training functions of Artificial Neural Networks (ANN) on Time series Forecasting
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 109 - Number 3 |
| Year of Publication: 2015 |
| Authors: Rashi Aggarwal, Rajendra Kumar |
10.5120/19168-0634
|
Rashi Aggarwal, Rajendra Kumar . Effect of Training functions of Artificial Neural Networks (ANN) on Time series Forecasting. International Journal of Computer Applications. 109, 3 ( January 2015), 14-17. DOI=10.5120/19168-0634
Abstract
Weather forecasting has been an area of considerable interest among researchers since long. A scientific approach to weather forecasting is highly dependent upon how well the atmosphere and its interactions with the various aspects of the earth surface is understood. Applicability of artificial neural networks (ANNs) in forecasting has led to tremendous surge in dealing with uncertainties. This paper focuses on analysis and selection of various techniques used in developing a suitable feed forward neural network for forecasting 24hr ahead hourly temperature using MATLAB 7. 6. 0 neural network toolbox. The data of 60 days hourly data temperature is used to train and test the different models, training functions, activation functions, learning functions, performance functions and the most suitable combination is selected. The performance and reliability of these models are then evaluated by number of statistical measures. Results are compared with each training function.
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