The cold start demand forecasting problem refers to the challenge of predicting future demand for a new product or service with little or no historical sales data available. This situation typically arises when a company introduces a new product or service to the market or a new product is launched in a store that is already being sold in other stores, and there is no past data available for training a machine learning model to predict future demand.
In traditional demand forecasting, historical sales data is used to train a machine learning model that can predict future demand. However, in the case of a new product, there is no historical data available. This presents a significant challenge because accurate demand forecasting is critical for making informed decisions about inventory, pricing, and marketing strategies.
About this Accelerator
This second accelerator of a three-part series on demand forecasting provides the building blocks for cold start modeling workflow on series with limited or no history. This notebook provides a framework to compare several approaches for cold start modeling.
The dataset consists of 50 series (46 SKUs across 22 stores) over a 2 year period with varying series history, typical of a business releasing and removing products over time. The test dataset contains 20 additional series with little or no history which were not present in the training dataset.
The previous notebookaims to inspect and handle common data and modeling challenges, identifies common pitfalls in real-life time series data, and provides helper functions to scale experimentation with the tools mentioned above and more.