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Course Outline

Forecasting with R

  • Introduction to Forecasting
  • Exponential Smoothing
  • ARIMA models
  • The forecast package

Package 'forecast'

  • accuracy
  • ACF
  • ARFIMA
  • ARIMA
  • ARIMA.errors
  • auto.arima
  • BATS
  • BoxCox
  • BoxCox.lambda
  • croston
  • CV
  • DM.test
  • DSHW
  • ETS
  • fitted.Arima
  • forecast
  • forecast.Arima
  • forecast.bats
  • forecast.ets
  • forecast.HoltWinters
  • forecast.lm
  • forecast.stl
  • forecast.StructTS
  • gas
  • gold
  • logLik.ets
  • MA
  • meanf
  • monthdays
  • MSTS
  • na.interp
  • naive
  • ndiffs
  • nnetar
  • plot.bats
  • plot.ets
  • plot.forecast
  • RWf
  • seasadj
  • seasonaldummy
  • seasonplot
  • SES
  • simulate.ets
  • Sindexf
  • splinef
  • subset.ts
  • taylor
  • TBATS
  • thetaf
  • tsdisplay
  • tslm
  • wineind
  • woolyrnq

Summary and Next Steps

Requirements

  • Fundamental knowledge of mathematics and statistics.
  • Programming experience in any language is recommended, though not mandatory.

Target Audience

  • Data analysts.
  • Business intelligence professionals.
  • Statisticians and researchers engaged in forecasting projects.
 14 Hours

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