Anthill Inside 2019

A conference on AI and Deep Learning

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A Guide on Dynamic Parameter Estimation for Causal Forecasting

Submitted by Tanmoy Bhowmik (@tanmoyb) on Tuesday, 16 April 2019

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Session type: Lecture


Executives now-a-days rely on forecasting approcahes in virtually any decision making. The use cases are ubiquitous in business and technology domains ranging from Demand/Sales forecasting in Supply Chain Management, Hiring/Attrition rate forecasting in HR operations, Predicting the cell traffic or netwrok health parameters for a Telecom business, just to name a few. The challenges faced in forecasting in general, are mainly because of inherent non-linearity and non-stationarity in the time series. In this talk, I will try to walk through a systematic approach for parameter estimation in forecasting of time series data with causal factors available in the light of statistical estimation theory.


  1. Forecasting Overview : Time Series based and Causal Forecasting
  2. Linear Estimation :
    i. Deterministic Estimation : Best Linear Unbiased Estimator and Sequential LSE
    ii. Bayesian Estimation : Sequential LMMSE and Kalman Filter
  3. Nonlinear Estimation : Extended Kalman Filter and Sequential Neural Network

Speaker bio

I did my PhD in Electrical Engineering from University of Texas at Arlington and am currently working as a Data Scientist in Ericsson Global AI team.


Preview video


  • Zainab Bawa (@zainabbawa) Reviewer 5 months ago

    Thank you for the submission, Tanmoy. Upload draft slides, explaining the content you will cover and emphasis on takeaways for participants. You should also upload a two-min elevator pitch video, explaining what this talk is about and why participants should be interested in hearing it. The deadline for uploading slides and videos is 1 May.

  • Tanmoy Bhowmik (@tanmoyb) Proposer 5 months ago

    Thank you. I uploaded the draft slides and the elevator pitch video.

  • Zainab Bawa (@zainabbawa) Reviewer 5 months ago

    This proposal will be more suited to Anthill Inside where we deal with concepts around AI, ML and Deep Learning.

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