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Alternative logins

We start fitting a regression model to predict the range of a car (in miles per gallon, `mpg`

) by its weight `wt`

. The scatterplot including regression line and residuals is shown below.

`fit <- lm(mpg ~ wt, data = mtcars)`

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What can we forecast?Which is easiest to forecast?Time series dataThe statistical forecasting perspectiveForecasting methods implemented in forecastRW-DRIFTARIMANNETARThe ts objectExerciseFrequencyFrequency of a time seriesClassical Time Series DecompositionExercise: Amazon RevenuesExercise: Calendar adjustmentsAutoregressive modelsDifferencingDifferencingMoving Average ModelsForecasting using ARIMAUS consumptionManual ARIMAAuto ARIMAM4 CompetitionConclusion from M4

Linear Regression

Regression: Residuals

Linear Regression

Regression: Residual Sum of Squares

Linear Regression

Regression: Fitting Wage

Linear Regression

Linear Model Selection and Regularization

Linear Regression

Stepwise Regression

Linear Regression

See how BIC and AIC compare with number of variables:

Linear Regression

Fit best model

Linear Regression

Classification Trees

Introduction

Classification Trees

Fitting Classification Trees

Classification Trees

Plotting Classification Trees

Classification Trees

Plotting Classification Trees

Classification Trees

Fitting Classification Tree using a Test Set

Classification Trees

Fitting a Classification Tree using Pruning and Cross Validation

Classification Trees

Apply Pruning

Classification Trees

Evaluate Pruned Tree

Classification Trees

Increase Number of best

Classification Trees

Forecasting

What can we forecast?

Forecasting

Which is easiest to forecast?

Forecasting

Time series data

Forecasting

The statistical forecasting perspective

Forecasting

Forecasting methods implemented in forecast

Forecasting

RW-DRIFT

Forecasting

ARIMA

Forecasting

NNETAR

Forecasting

The ts object

Forecasting

Exercise

Forecasting

Frequency

Forecasting

Frequency of a time series

Forecasting

Classical Time Series Decomposition

Forecasting

Exercise: Amazon Revenues

Forecasting

Exercise: Calendar adjustments

Forecasting

Autoregressive models

Forecasting

Differencing

Forecasting

Differencing

Forecasting

Moving Average Models

Forecasting

Forecasting using ARIMA

Forecasting

US consumption

Forecasting

Manual ARIMA

Forecasting

Auto ARIMA

Forecasting

M4 Competition

Forecasting

Conclusion from M4

Forecasting

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