Learn how to use TimeGPT to forecast sales scenarios with different pricing strategies.
1. Import required packages
2. Load the M5 dataset
DataFrame | Contents |
---|---|
Y_df | Historical sales (y ) for each product (unique_id ) and date (ds ). |
X_df | Additional information (exogenous variables) such as sell_price . |
sell_price
column:3. Forecast demand using price as an exogenous variable
FOODS_1_129_
) across all 10 stores. This product exhibits frequent price changes, making it ideal for modeling price effects on demand.y
) and price (sell_price
) data into one DataFrame:df_train
)
• Future exogenous data (future_ex_vars_df
)model="azureai"
in the forecast
call. Refer to the official tutorials for more details.4. 'What if?' forecasting: exploring price changes
What is Price Elasticity?
Important Considerations