Time series forecasting for the adobe software company’s stock prices using ARIMA (BOX-JENKIN’) model

The time series forecasting strategy, Auto-Regressive Integrated Moving Average (ARIMA) model, is applied on the time series data consisting of Adobe stock prices, in order to forecast the future prices for a period of one year. ARIMA model is used due to its simple and flexible implementation for s...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2021-11, Vol.2115 (1), p.12044
Main Authors: Vaibhava Lakshmi, R., Radha, S.
Format: Article
Language:English
Subjects:
ARIMA
Stock prices
Time series forecasting
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The time series forecasting strategy, Auto-Regressive Integrated Moving Average (ARIMA) model, is applied on the time series data consisting of Adobe stock prices, in order to forecast the future prices for a period of one year. ARIMA model is used due to its simple and flexible implementation for short term predictions of future stock prices. In order to achieve stationarity, the time series data requires second-order differencing. The comparison and parameterization of the ARIMA model has been done using auto-correlation plot, partial auto-correlation plot and auto.arima() function provided in R (which automatically finds the best fitting model based on the AIC and BIC values). The ARIMA (0, 2, 1) (0, 0, 2) [12] is chosen as the best fitting model, with a very less MAPE (Mean Absolute Percentage Error) of 3.854958%.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2115/1/012044