Building models that are able to capture external data is often a key aspect of time series forecasting projects. For instance: Recently-observed activity on an e-commerce website can help predict future sales. Observed rainfalls and known weather forecasts can help to predict hydro and solar electricity production. Making the model…

Advanced analytics and artificial intelligence (AI) are becoming increasingly important for the chemical industries. In the past, chemical companies were often relying on analytics tools that now appear fairly basic for making critical decisions within research and development (R&D), manufacturing, creation, supply chain and sales. Today, the ability of a…

Any quantity varying over time can be represented as a time series: sales numbers, rainfalls, stock prices, CO2 emissions, Internet clicks, network traffic, etc. Time series forecasting — the ability to predict the future evolution of time series— is thus a key capability in many domains where anticipation is important…

Time series simply represent data points over time. They are thus everywhere in nature and in business: temperatures, heartbeats, births, population dynamics, internet traffic, stocks, inventories, sales, orders, factory production — you name it. In countless cases, efficient processing and forecasting of time series has the potential to provide decisive…

In general, a neural network is a multivariate, vector-valued function looking like this: