Although ARIMA models can be highly accurate and reliable under the appropriate conditions and data availability, one of the key limitations of the model is that the parameters (p, d, q) need to be manually defined; therefore, finding the most accurate fit can be a long trial-and-error process.
Similarly, the model depends highly on the reliability of historical data and the differencing of the data. It is important to ensure that data was collected accurately and over a long period of time so that the model provides accurate results and forecasts.
Title
What is the Autoregressive Integrated Moving Average (ARIMA)?
URL
https://corporatefinanceinstitute.com/resources/knowledge/other/autoregressive-integrated-moving-average-arima/
Summary
Key Points
Although ARIMA models can be highly accurate and reliable under the appropriate conditions and data availability, one of the key limitations of the model is that the parameters (p, d, q) need to be manually defined; therefore, finding the most accurate fit can be a long trial-and-error process.
Similarly, the model depends highly on the reliability of historical data and the differencing of the data. It is important to ensure that data was collected accurately and over a long period of time so that the model provides accurate results and forecasts.
Citation
“Autoregressive Integrated Moving Average (ARIMA).” Corporate Finance Institute, 20 Jan. 2022, https://corporatefinanceinstitute.com/resources/knowledge/other/autoregressive-integrated-moving-average-arima/.
Repo link