The problem was approached as a regression problem with a continuous spread of outputs representing product formation. Many widely used time-series specific approaches, such as autoregressive integrated moving average, were unsuitable for this problem as they assume a static output with only seasonal variations. This contrasts with the task described here that involves predicting an ever-increasing product amount. However, the approaches we use, such as recurrent neural networks, are suitable for application to time-series and do not assume a stationary output.