{"id":13823,"date":"2018-09-13T11:38:23","date_gmt":"2018-09-13T09:38:23","guid":{"rendered":"https:\/\/www.inovex.de\/blog\/?p=13823"},"modified":"2023-01-05T14:37:19","modified_gmt":"2023-01-05T13:37:19","slug":"time-series-forecasting-machine-learning","status":"publish","type":"post","link":"https:\/\/www.inovex.de\/de\/blog\/time-series-forecasting-machine-learning\/","title":{"rendered":"Time Series Forecasting with Machine Learning Models"},"content":{"rendered":"

Recently, Machine Learning (ML) models have been widely discussed and successfully applied in time series forecasting tasks (Bontempi et al., 2012). In this blog article we explain an exemplary process of how time series forecasting tasks can be solved with machine learning models, starting with the problem modeling and ending with visualizing the results by embedding the models in a web app for demonstration purposes. To illustrate this process we use a real-world example: forecasting ride-hailing demand based on historical bookings.<\/p>\n

Ride-hailing is a new form of On-Demand Mobility (ODM), where a customer can digitally hail a ride via a mobile application. Common examples for ride-hailing services are Uber or Lyft. Offering a service that is both cost effective and of high quality for the customer requires the efficient addressing of the supply- demand disequilibrium, which can be alleviated by accurately predicting passenger demand.<\/p>\n

In the following we will first explain how to model the forecasting problem and then describe how embedding the models in a web app can be helpful to communicate and assess the forecasting results.<\/p>\n

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