Time Series Forecasting

Nov 16, 2019



Python programming experience Basics of machine learning

Trying to master time series but finding it too complex? We have designed this comprehensive workshop just for you! Learn the core components and techniques for time series analysis, how to build time series models in Python, and much more!

Pre-requisites for this Workshop

  • Python programming experience
  • Basics of predictive modeling
Key Takeaways from the Workshop
  • A clear and concise understanding of when to apply Time Series models and how much to rely on Time Series Analysis
  • How to perform time series analysis with Python to facilitate forecasting, hypothesis testing and catastrophic event prediction
  • A good understanding of Stationarity in Time Series and its importance in forecasting
  • The Wold's Theorem giving rise to many popular Time Series modelling techniques
  • Time series in Python with regression, Holt-Winter's approach, Box Jenkins models, the parsimonious AR-MA and the automatic selection with the popular ARIMA model
  • Discussions on which scenarios fit for which kind of models. Various examples in Python to clarify understanding
  • Discussions on next generation time series models and further study guidance
  • Rohan Rao

    Data Scientist

    H2O.ai & Kaggle GrandMaster


    Rohan Rao (a.k.a. ‘vopani’) currently works as a Data Scientist @ H2O.ai. He is a post-graduate in Applied Statistics from IIT-Bombay and part of elite group of Kaggle Grandmasters. His core expertise lies in driving, pipelining and building Machine Learning solutions hands-on. Rohan’s work has revolved around leading small teams and architecting end-to-end ML-driven solutions

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