R is an open source programming language for statistical computing, data analysis, and graphical visualization. R has an estimated one million users worldwide, and its user base is growing. While most commonly used within academia, in fields such as computational biology and applied statistics, it is gaining currency in commercial areas such as quantitative finance and business intelligence.
Among R's strengths as a language are its powerful built-in tools for inferential statistics, its compact modeling syntax, its data visualization capabilities, and its ease of connectivity with persistent data stores (from databases to flatfiles).
In addition, R's open source nature and its extensibility via add-on "packages" has allowed it to keep up with the leading edge in academic research.
For all its strengths, though, R has an admittedly steep learning curve; the first steps towards learning and using R can be challenging.
To this end, the Bay Area R Users Group is dedicated to bringing together area practitioners of R to exchange knowledge, inspire new users, and spur the adoption of R for innovative research and commercial applications.
We are trying something new for BARUG, offering a Saturday morning workshop where we can spend more time "hands on" working with R code. Rami Krispin will present our first workshop on Time Series Analysis. Rami is a data scientist that focuses on time series analysis and forecasting. He is the author of "Hands-On Time Series Analysis with R" and several R packages, including the TSstudio package for time series analysis and forecasting applications.
The workshop is free but seating is limited. If you do not RSVP for the event you may not get in.
The agenda for the morning is as follows:
8:00 AM - doors open
8:30 Workshop Begins
8:30 - 9:00 Introduction to time series analysis and forecasting
a) An overview of the forecasting process
b) Packages and tools
9:30 - 10:00 Time series objects - introduction to the time series classes and their attributes:
b) xts and zoo
10:00 - 10:30 Descriptive analysis of time series:
a) Correlation analysis - acf and pacf functions, lags plots, etc.
b) Seasonal analysis
10:30 - 10:45 Break
10:45 - 11:30 Linear regression-based forecasting models:
a) Feature engineering - decomposing the time series
components into regression features
b) Handling special events, holidays, outliers, etc
11:30 - 12:30 The ARIMA family of models:
a) Stationarity and differencing
b) AR process
c) MA process
d) ARMA and ARIMA model
e) Seasonal ARIMA
f) Linear regression with ARIMA errors