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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.

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Techniques presented

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

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Tutorials:

​There are exercises for each of the topics covered.

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Prerequisites

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

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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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