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FRCT training

Demand and Sales Forecasting

Goals

Concepts for managing an archive of historical data, carrying out operations of selection, transformation, aggregation, etc.

Techniques presented

Preliminary analysis of the historical series, through graphical representations and study of the autocorrelations.

Tutorials:

There are exercises for each of the topics covered.

Prerequisites

Attendance at the INTRO course or having an intermediate knowledge of Statistics for Data Analysis is a prerequisite.

Subjects:

The linear regression

  • Recall of simple linear regression

  • multiple linear regression

    • Hypothesis testing on parameters

    • automatic selection of variables

    • Residual analysis

    • Multicollinearity

    • Using the model for prediction and simulation

  • Introduction to the generalized linear model

    • The analysis of variance

    • Mixed effects models

    • Multivariate analysis of variance and repeated measures

 

Introduction to time series analysis

Definition of time series

What is a good forecast

Conceptual scheme of a forecasting system

Choosing a model

Forecasting analysis applications

  • Useful operations and features of Statistics for Data Analysis

  • Regression models and their applications

 

Econometric models: econometrics and regression

The decompositional approach

  • Multiplicative and additive model in the treatment of seasonal components

 

Exponential leveling

  • Forecasting with levelings

 

Stochastic processes

  • ARIMA models

    • Building a  seasonal arima model

  • Intervention analysis: elements related to ARIMA models

    • Prediction and simulation with ARIMA models using dummy variables

    • Treatment of structural breaks with ARIMA models

  • ARMAX models: use of covariates

  • Multivariate models: standard VAR models

 

Performance of forecasting models, introduction to simulation and automatic forecasting.

 

 

 

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