Forecasting plays a very crucial role in business planning across a plethora of industries from Entertainment to Finance to Healthcare. In time-series forecasting, the presence of a time component implicitly brings in a lot of contributing variables making this an especially interesting branch of machine learning. In this session we will take a bird’s eye view of different state-of-the-art tools, techniques & implementations currently in use for time-series forecasting from classical forecasting to AutoML & deep-learning, and how to deploy them at scale. The discussion would also include a case-study, with live code, of how to best approach a time-series problem, and choose the right algorithm, for economic forecasting in the context of Covid outbreak, which adds unprecedented uncertainties to the problem.

Instructor's Bio

Monica Ravipudi, Data Scientist at The Economist Intelligence Unit

Monica is a Data Scientist with the Healthcare division of The Economist Group responsible for building & managing robust data pipelines along with deploying advanced analytics techniques to generate actionable insights.

After completing an Integrated Masters in Economics, Monica worked as a consultant on data driven strategy development to maximise active consumer base for an entertainment media client and witnessed how data shaped the marketing and budgetary decisions at an organisation level. This stint in analytics during her early professional career eventually  paved way to a Masters in Business Analytics at National University of Singapore (NUS).

Having worked on a diverse spectrum of projects revolving around FMCG & entertainment industries and now in healthcare Monica constantly strives to learn and catch up with this ever evolving space & create a social impact with her work.

Apart from her professional career Monica’s interests lie in travelling, exploring new places and cuisines with an always open ear for new suggestions.

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    Adapting Time-Series Forecasting to Uncertain Scenarios

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