ARIMA Model for Accurate Time Series Stocks Forecasting

Shakir Khan,Hela Alghulaiakh

doi:10.14569/ijacsa.2020.0110765

Abstract

With the increasing of historical data availability and the need to produce forecasting which includes making decisions regarding investments, in addition to the needs of developing plans and strategies for the future endeavors as well as the difficulty to predict the stock market due to its complicated features, This paper applied and compared auto ARIMA (Auto Regressive Integrated Moving Average model). Two customize ARIMA(p,D,q) to get an accurate stock forecasting model by using Netflix stock historical data for five years. Between the three models, ARIMA (1,1,33) showed accurate results in calculating the MAPE and holdout testing, which shows the potential of using the ARIMA model for accurate stock forecasting.

Highlights

The increasing availability of historical data with the need for production forecasting has attracted the attention of Time Series Forecasting (TSF), which gives a sequence of predicting future values, especially with the limitations of traditional forecasting, such as complexity and time-consuming [1]
Within the measurement of Mean Absolute Percentage Error (MAPE), the accuracy was 99.74% and autoregressive integrated moving average (ARIMA) (1,2,33) was 99.75% which is almost the same
The research used Netflix stocks historical data for the past five from 7 April 2015 to 7 April 2020 to compare the results of auto ARIMA model and two customize ARIMA (p,D,q) models

Summary

Introduction

The increasing availability of historical data with the need for production forecasting has attracted the attention of Time Series Forecasting (TSF), which gives a sequence of predicting future values, especially with the limitations of traditional forecasting, such as complexity and time-consuming [1]. The empowered institutions and individuals to make decisions to invest and the need to develop plans and strategy of future endeavors made the prediction exciting area for the domain researchers to work and improve the predictive models [3, 4, 5]. To get the best result of the stock market, forecasting stock prices become an attractive pursuit for investors. Several models and techniques in the past years have been developed to stock prices prediction. Data in time series included as points listed in time order, which is sequence of discrete-time space in time, where the forecasting will be predicting the future by analyzing observed points in the series [7]

Methods

Results

Conclusion