Forecasting Time Series Data with Artificial Neural Network of Bayesian Regularization

Doni El Rezen Purba, Herman Mawengkang, Tulus

2018

Abstract

Forecasting or predicting future events is important to take into account in order for an activity to proceed properly. Flights predict the weather forecast, the banking industry predicts the price of currency, the health world predicts the disease, the retail business predicts total sales. prediction or forecasting of events is calculated using past data, usually in the form of time series. Artificial neural networks are capable of forecasting time-series data. Forecasting results with artificial neural network is influenced from the network architecture model is determined, one of which determination of training function. Based on research conducted by Aggarwal KK (et al 2005) and Murru & Rossini, R. (2016), using Bayesian regularization training function in their research, this research uses the algorithm for time clock data forecasting process with several model of layer count and number of neurons. The results obtained with the number of 3 layers and each neuron of 36, 12, 6 for the best process performance, and the number of neurons 24, 12, 6 for the shortest iteration process.

Paper Citation

in Harvard Style

Rezen Purba D., Mawengkang H. and Tulus. (2018). Forecasting Time Series Data with Artificial Neural Network of Bayesian Regularization. In Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology - Volume 1: ICEST, ISBN 978-989-758-496-1, pages 564-568. DOI: 10.5220/0010046505640568

in Bibtex Style

@conference{icest18,
author={Doni El Rezen Purba and Herman Mawengkang and Tulus},
title={Forecasting Time Series Data with Artificial Neural Network of Bayesian Regularization},
booktitle={Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology - Volume 1: ICEST,},
year={2018},
pages={564-568},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010046505640568},
isbn={978-989-758-496-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology - Volume 1: ICEST,
TI - Forecasting Time Series Data with Artificial Neural Network of Bayesian Regularization
SN - 978-989-758-496-1
AU - Rezen Purba D.
AU - Mawengkang H.
AU - Tulus.
PY - 2018
SP - 564
EP - 568
DO - 10.5220/0010046505640568