Скачать с ютуб Predicting New York City Taxi demand: spatio-temporal Time Series Forecasting | Fabian Hertwig в хорошем качестве

Predicting New York City Taxi demand: spatio-temporal Time Series Forecasting | Fabian Hertwig

Speaker: Fabian Hertwig (MaibornWolff GmbH) | https://mlconference.ai/speaker/fabia... Time series forecasting has always been an important field in machine learning and statistics, as it helps us to make decisions about the future. A special field is spatio-temporal forecasting, where predictions are not only made on the temporal dimension, but also on a regional dimension. In this session, we will present a demonstration project to predict taxi demand in Manhattan, NYC for the next hour. We’ll show some of the basic principles of time series forecasting and compare different models suited for the spatio-temporal use case. Therefore, we will take a closer look at the principles of models like long short-term memory networks and temporal convolutional networks. We will show that these models decrease the prediction error by 40% as compared to a simple baseline model, which predicts the same demand as in the last hour. 😊 Come, join us at the next Machine Learning Conference | https://mlconference.ai 👉 Follow us on Twitter |   / mlconference   👍 Like us on Facebook |   / mlconference