Anthill Inside 2019

A conference on AI and Deep Learning

A Guide on Dynamic Parameter Estimation for Causal Forecasting

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

Session type: Lecture Status: Rejected

Abstract

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.

Outline

  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.

Slides

https://drive.google.com/file/d/1ktY2EGDIlaDMl7OBHdpJ4Q9dLsZ55DwB/view?usp=sharing

Preview video

https://www.youtube.com/watch?v=qJ4qmp2kdi8

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