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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
- Basic general maths and statistics skills
- Programming in any language recommended but not necessary
Audience
- Data analysts
- Business intelligence professionals
- Statisticians and researchers involved in forecasting projects
14 Hours
Testimonials (4)
Well thought out and high grade planning materials.
Andrew - Office of Projects Victoria - Department of Treasury & Finance
Course - Forecasting with R
he is patient
Abdul De kock - Vodacom
Course - Forecasting with R
I genuinely liked his knowledge and practical examples.
Irina Tulgara
Course - Forecasting with R
A lot of knowledge - theoretical and practical.