Why is the ARIMA model good?

It is widely used in demand forecasting, such as in determining future demand in food manufacturing. That is because the model provides managers with reliable guidelines in making decisions related to supply chains . ARIMA models can also be used to predict the future price of your stocks based on the past prices.

ARIMA models are not generally preferred over any other time series analysis method . There are certainly not preferred when the series demonstrate non-stationaries unable to be modelled using the ARIMA framework.

ARIMA (1,1,33) model showed better accuracy. Although within the measurement of MAPE, the accuracy was 99.74% and ARIMA (1,2,33) was 99.75% which is almost the same. However, owing to its result from holdout test it is considered the best accuracy among the three models.

The ARIMA models have proved to be excellent short-term forecasting models for a wide variety of time series.

Autoregressive integrated moving average (ARIMA) models predict future values based on past values . ARIMA makes use of lagged moving averages to smooth time series data. They are widely used in technical analysis to forecast future security prices.

ARIMA is a form of regression analysis that indicates the strength of a dependent variable relative to other changing variables. The final objective of the model is to predict future time series movement by examining the differences between values in the series instead of through actual values .

ARIMA (Autoregressive Integrated Moving Average): ARIMA is arguably the most popular and widely used statistical technique for forecasting .