Demand Forecasting using multivariate time Series forecasting

Question

I am currently working with a multivariate time series data which has these columns: Order_date, store_id, region, product_ID, Unit_sold, discount, holiday(yes/no), etc. There are in total 50 unique products. I have to perform demand forecasting of each product. I want to implement the SARIMAX model to this dataset.

I am a novice in time series. Please guide me through the steps.

Answer 1

Let's say for instance you sell sweaters, IKEA furniture, and ice cream. Now you know that the trading of the sweaters will be more just before and during winter, IKEA furniture sells best during weekends but is fairly even throughout the year, and ice cream sells best in summer, but mostly when it's hot. If you implement a time series model to all these at once, even though the products might all show trends with the same periodicities, their results will be completely opposite!

Yes, more people will buy ice cream, sweaters, and furniture during weekends, but the impact of it being a weekend will be much larger for the last one than for others. And sweaters and ice cream apparently both show yearly trends, but in opposite ways.

I would now like to advise you to create a model for one product, then look into automating the method, and for the rest of the products, just analyze the results of the automation process.

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