@article{
 title = {Forecasting Gold Price: An Application of Auto Regressive Integrated Moving Average Model},
 type = {article},
 year = {2019},
 keywords = {ARIMA Model. .,Forecasting,Gold Price,Model Selection},
 pages = {115-121},
 volume = {58},
 id = {a55b1999-9ae3-328f-a6f1-0e74d5b64b2d},
 created = {2021-05-05T16:05:40.114Z},
 file_attached = {false},
 profile_id = {3d6b17c2-7de8-3a82-bf4f-ddb0e3081e5f},
 last_modified = {2022-05-06T21:42:23.804Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {In recent years, the trend of the global gold price has attracted a lot of attention and the price of gold has terrifying spike compared to historical trend. In this paper, an attempt has been made to develop a model for forecasting the gold price. The sample data of gold price (in USD per ounce) were taken from January, 1950 to January, 2018. This paper uses the Box-Jenkin's Auto Regressive Integrated Moving Average (ARIMA) methodology for building the forecasting model. Results advocate that ARIMA(1,1,2)(1,1,2)12 is the most suitable model to be used for predicting the gold price.},
 bibtype = {article},
 author = {Faruk, M.O. and Hossain, M.M.},
 journal = {International Journal of Applied Mathematics and Statistics™},
 number = {4}
}

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