Predicting New York City Taxi demand: Spatio-temporal time series forecasting
Machine learning can help predict things dependent on time such as taxi demand. 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 from the Machine Learning Conference, Fabian Hertwig presents a demonstration project to predict taxi demand in Manhattan, NYC for the next hour. He shows some of the basic principles of time series forecasting and compare different models suited for the spatio-temporal use case.
Take a closer look at the principles of models like long short-term memory networks and temporal convolutional networks. This talk 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.
Fabian Hertwig I am a Data Scientist at MaibornWolff in Munich. I fell in love with problem solving in data four years ago when I was a working student and helped an automotive company to improve its processes by analyzing data. Since then, I specialized in Data Science and Deep Learning and worked on various projects for the last three years.