Deep Learning for Time Series Forecasting, Anomaly Detection and Classification

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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.
Speaker: Isaac Godfried
  1. AICamp
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I see you do a lot of amazing efforts in your framework. Thank you for your efforts. Can please do one lesson on one model only to show us how to use your library to forecast something, for example using Transomer Decoder to forecast the number of deaths of Covid. I found it is difficult to use your library without clear instructions

thelastone
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thanks a lot for this nice presentation

thelastone
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Can I upload your code on the weight and bias framework and use it for my prediction task?

thelastone