Answer :
For the prediction of univariate time series data, Autoregressive Integrated Moving Average, or ARIMA, is one of the most used forecasting techniques.
With examples, define the ARIMA model.
Based on its own historical values, the ARIMA model forecasts a given time series. It can be used to any non-seasonal number series that shows patterns and is not a collection of random occurrences. For instance, sales information from a clothes business would be a time series since it was gathered over time.
Which forecasting approach is more effective for seasonal data?
This approach works best with data that has a pattern and seasonality but does not get stronger with time. As a result, the forecast is curved, highlighting the seasonal fluctuations in the data.
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