Deep Learning for Time Series Forecasting, Anomaly Detection and Classification

Deep learning has made impressive strides with respect to time series forecasting and classification. DL models have recently shattered time-series research benchmarks yet remain seldom used in the industry.

In this seminar, we will discuss how to use deep learning to forecast and classify real world time series datasets in healthcare, climate, and agriculture using Flow Forecast, a deep learning for time series framework built in PyTorch. We will also discuss some of the latest advances and cutting edge research in the time series field.

Isaac Godfried

Isaac Godfried is a data scientist and AI researcher focused on applying deep learning to high impact areas in health, climate, agriculture, and cyber security.
  • Date: Nov 15, 13:00 (US Pacific Time)
  • Fee: Free
  • Available Seats: 261 (max 500)
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