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# Python uses ARIMA and arimax to predict the time series data of store commodity sales demand

2022-07-25 12:59:35【51CTO】

This paper explores different time series techniques on relatively simple data sets .

Given 5 Store sales data for , And ask you to predict 10 From different stores 50 Different products in 3 Sales within months .

What is the best way to deal with seasonality ？ The store should be modeled separately , They can still be combined ？

## Store project demand forecast

### Autoregressive composite moving average (ARIMA)

this ARIMA The model can be applied to nonstationary time series ARMA Generalization of the model .

### Load data

All stores seem to show the same trend and seasonality .

### ARIMAX

Autoregressive composite moving average with explanatory variables (ARIMAX) yes ARIMA Extended version of , These include independent predictors .

## Prepare the data

## Build the model

### Make predictions

## Example forecast

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author[51CTO],Please bring the original link to reprint, thank you.

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