Igor Gotlibovych: Deep Learning and Time Series Forecasting for Smarter Energy | PyData London 2019

Slides - https://tech.octopus.energy/data-discourse/PyData2019/TimeSeries.html Balancing the supply and demand of electrical energy relies heavily on accurate forecasting and probabilistic decision-making. In this talk, we will aim to demystify time series forecasting, and demonstrate how a single forecasting framework built on pandas, scikit and tensorflow allows us to extend simple models by applying transfer learning, auto-encoders and stochastic modelling. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps