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Duration 14 hours
Course Outline
Time Series Projection Using the R Programming Language
- Overview of Projection Methodologies
- Exponential Smoothing Techniques
- Auto-Regressive Integrated Moving Average (ARIMA) Frameworks
- Implementation of the 'forecast' Module
'forecast' Module Capabilities
- 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
Recap and Forward Path
Requirements
- Proficiency in foundational mathematics and statistical concepts.
- Experience with programming languages is advisable but not mandatory.
Intended Participants
- Data Analysts
- Business Intelligence Professionals
- Statisticians and Researchers engaged in projection initiatives.
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.