Predicting Closed Price Time Series Data Using ARIMA Model

Sadam Al Wadi · Mohammad Salameh Almasarweh · Ahmed Atallah Alsaraireh

64 citations0/0 files storedModern Applied Science

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

Closed price forecasting plays a main rule in finance and economics which has encouraged the researchers to introduce a fit model in forecasting accuracy. The autoregressive integrated moving average (ARIMA) model has developed and implemented in many applications. Therefore, in this article the researchers utilize ARIMA model in predicting the closed time series data which have been collected from Amman Stock Exchange (ASE) from Jan. 2010 to Jan. 2018. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.

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References

1977Time Series Analysis: Forecasting and Control 1971Time Series Analysis Forecasting and Control.2004A hybrid ARIMA and support vector machines model in stock price forecasting 2011Stock index forecasting based on a hybrid model 2008Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs)2010Forecasting stock exchange movements using neural networks: Empirical evidence from Kuwait 1998Forecasting irish inflation using ARIMA models 2012Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs)

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