TS-4: Sales and demand forecasting

TL;DR

This analysis explores different techniques for sales and demand forecasting, including the crosstalk model, machine learning approach, and forecasting for new products.

Transcript

hello everyone and welcome to my youtube channel today we have once again it's conrad and he's going to talk something about he's one of the like most recurring guests on my channel now like four four five five episodes i i guess uh including one previously and this one yeah so it's always it's always good to see you and uh thank you thank you like... Read More

Key Insights

• ❓ The crosstalk model is effective for sales and demand forecasting when there is periodicity in the data.
• 🎏 Lagged features are valuable in capturing the temporal dynamics of sales data in the machine learning approach.
• 👶 Forecasting for new products requires innovative techniques such as multi-output regression and clustering.

Questions & Answers

Q: How does the crosstalk model work for sales and demand forecasting?

The crosstalk model uses exponential smoothing to estimate the level of sales and the time it takes between non-zero demand occurrences. These estimates are then combined to make predictions.

Q: What is the advantage of using lagged features in the machine learning approach?

Lagged features allow us to incorporate the past values of each series into the regression model, capturing the temporal dynamics of sales data and improving prediction accuracy.

Q: How does forecasting for new products differ from forecasting for existing products?

Forecasting for new products is challenging because there is no sales history to leverage. Techniques like multi-output regression and clustering can be used to make predictions based on the characteristics of similar existing products.

Q: How do you handle outliers in sales forecasting?

The approach to handling outliers depends on the model being used. In some cases, it may be best to remove outliers, while in others, it may be important to keep them and adjust the distribution or use specialized models that can accommodate outliers.

Summary & Key Takeaways

• The crosstalk model is based on exponential smoothing and is used to predict sales by analyzing periodicity in sales data.

• The machine learning approach involves reducing the sales forecasting problem to a regression problem using lagged features and categorical variables.

• Forecasting for new products is challenging due to the lack of sales history, but techniques like multi-output regression and clustering can be used to make predictions.