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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
Testimonials (5)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
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.