Forecast and trend analysis of gold prices in India using auto regressive integrated moving average model

J. Surendra, K. Rajyalakshmi, B.V. Apparao, G. Charankumar, Abhishek Dasore

Abstract


Autoregressive Integrated Moving Average (ARIMA) is one of the powerful statistical method to forecast the timeseries data. Forecasting plays a key role in estimating the future prices. Keeping in view that we have selected the prices of Gold in India from 1964 to 2019 through different secondary sources. Many factors are responsible for rise of gold price in India such as traditional demand, no liability on the investors, inflation proof, low interest rate on most of the saving schemes, safe investment tool.   In the present study we mainly focused on estimating the prices of Gold from the year 2020 to 2029, observed the sudden increase of gold price in 2020 due to various reasons and its impact on forecasting prices of gold using ARIMA model. Identifying the suitable ARIMA model (0,2,3) by conducting the analysis of Autocorrelation function (ACF) and Partial autocorrelation function (PACF) to the selected differenced series and present the forecasting prices of gold.  The selected secondary source data exhibits the positive trends for the quantitative analysis.

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Published: 2021-01-27

How to Cite this Article:

J. Surendra, K. Rajyalakshmi, B.V. Apparao, G. Charankumar, Abhishek Dasore, Forecast and trend analysis of gold prices in India using auto regressive integrated moving average model, J. Math. Comput. Sci., 11 (2021), 1166-1175

Copyright © 2021 J. Surendra, K. Rajyalakshmi, B.V. Apparao, G. Charankumar, Abhishek Dasore. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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