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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.

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