ARIMA

What is ARIMA?

ARIMA (AutoRegressive Integrated Moving Average) is an extension of the another forecasting algorithm, ARMA, that incorporates differencing (integration) to handle non-stationary time series data. Non-stationary data refers to data that exhibits trends, seasonality, or other time-dependent patterns.

What are the components of ARIMA?

• Differencing: If the data is non-stationary, differencing is applied to make it stationary. Differencing involves calculating the differences between consecutive observations to remove trends or seasonality.
• Autoregressive (AR) and Moving Average (MA) components: After differencing, the AR and MA components are applied to the differenced data to capture any remaining autocorrelation and moving average patterns.

This modeling logic is embedded in Hakio’s approach: The platform's forecasting engine uses ARIMA insights for the Fashion Planning Module to time collections and channels, while its Sales & Operations Module keeps core styles available as demand patterns evolve.

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