Ryuta Yoshimatsu

Jul 27, 2024 | medium.com | Ryuta Yoshimatsu

Developed from ground up with this best practice in mind is Databricks' Many Model Forecasting (MMF), designed to bootstrap your forecasting solutions. MMF accelerates the development of sales and demand forecasting solutions by facilitating the implementation of all essential stages, including data preparation, training, backtesting, cross-validation, scoring, and deployment. MMF adopts a configuration-over-code approach, minimizing the need for extensive coding to get started.

Jul 26, 2024 | databricks.com | Nate Wardwell |Sam Sawyer |Bryan Smith |Ryuta Yoshimatsu

IntroductionTime series forecasting serves as the foundation for inventory and demand management in most enterprises. Using data from past periods along with anticipated conditions, businesses can predict revenues and units sold, allowing them to allocate resources to meet expected demand.

Jul 1, 2023 | medium.com | Ryuta Yoshimatsu

This is a simple linear regression model that we have all seen:where i signifies the iᵗʰdata point, θ are the model coefficients and e is the noise term. Now, this is a dynamic linear model:The difference between the two models is that the parameters in the dynamic linear model are time dependent. We can see that in the subscripts t. This means that our coefficients of the regressors in the dynamic linear model can change over time.