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A Study on Forecaster Model using Time Series Data

Ashwini N., Rajshekar Patil M.. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Ashwini N., Rajshekar Patil M.
10.5120/cae2017652604

Ashwini N. and Rajshekar Patil M.. A Study on Forecaster Model using Time Series Data. Communications on Applied Electronics 7(2):34-39, May 2017. BibTeX

@article{10.5120/cae2017652604,
	author = {Ashwini N. and Rajshekar Patil M.},
	title = {A Study on Forecaster Model using Time Series Data},
	journal = {Communications on Applied Electronics},
	issue_date = {May 2017},
	volume = {7},
	number = {2},
	month = {May},
	year = {2017},
	issn = {2394-4714},
	pages = {34-39},
	numpages = {6},
	url = {http://www.caeaccess.org/archives/volume7/number2/739-2017652604},
	doi = {10.5120/cae2017652604},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Many physical and artificial phenomena can be described by time series. The prediction of any such phenomenon could be complex and interesting. The ability to forecast the future is mainly based on only past data, which leads to strategic advantages and will be key to success in organizations. Time series forecasting allows the modeling of complex systems as black-boxes, being a focus of attention in several research arenas. There are several methods for time series data which mainly depends whether the data is linear or nonlinear. In this paper a survey on the forecasting method based on the different types of the data presented. This survey will mainly concentrate based on neural network, evolutionary computation etc. in solution development of forecasting models and rules, continued with hybrid forecaster mainly.

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Keywords

Moving Average(MA), Autoregressive(AR), Neural Networks, Genetic algorithms, Time series forecasting, Autoregressive Integrated Moving Area(ARIMA) methods, Generalized autoregressive conditionally heteroskedastic(GARCH) methods, Artificial Neural Networks(ANN)