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
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Recall of simple linear regression
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multiple linear regression
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Hypothesis testing on parameters
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automatic selection of variables
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Residual analysis
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Multicollinearity
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Using the model for prediction and simulation
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Introduction to the generalized linear model
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The analysis of variance
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Mixed effects models
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Multivariate analysis of variance and repeated measures
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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
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Useful operations and features of Statistics for Data Analysis
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Regression models and their applications
Econometric models: econometrics and regression
The decompositional approach
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Multiplicative and additive model in the treatment of seasonal components
Exponential leveling
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Forecasting with levelings
Stochastic processes
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ARIMA models
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Building a seasonal arima model
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Intervention analysis: elements related to ARIMA models
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Prediction and simulation with ARIMA models using dummy variables
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Treatment of structural breaks with ARIMA models
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ARMAX models: use of covariates
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Multivariate models: standard VAR models
Performance of forecasting models, introduction to simulation and automatic forecasting.