Time Series Forecasting for Cold-Start Items by Learning from Related Items using Memory Networks

Time series forecasting for new items is very important in a wide variety of applications. Existing solutions for time series forecasting, however, do not address this cold start problem. The underlying machine learning models in these solutions rely heavily on the availability of the past data points of the time series. Here, we propose to use a modified Dynamic Key-Value Memory Network (DKVMN) that enables knowledge sharing across items. The network is conventionally used for binary tasks in knowledge tracing. We modify it for our regression-based forecasting use-case. Specifically, we change the output layer, include feedback for error correction, add a mechanism to handle scale across items. We test our solution on the SKU level data of a large e-commerce company and compare the results to the widely used LSTM model, outperforming it by over 25% across multiple metrics.