class: center, middle, inverse, title-slide # Time Series ## ⚔<br/>with xaringan ### Prof. Eric A. Suess ### 2019/01/25 (updated: 2019-01-25) --- background-image: url(https://upload.wikimedia.org/wikipedia/commons/b/be/Sharingan_triple.svg) ??? Image credit: [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Sharingan_triple.svg) --- class: center, middle # Time Series Today we are going to get started thinking about the ideas related to understanding time series data. Start by thinking data is not independent in time! --- class: inverse, center, middle # Get Started --- # Books to consider - The book I am going to suggest is Forecasting: Principles and Practice [fpp2](https://otexts.com/fpp2/). This is an very nice book, now with all of its code written using the tidyverse. - The second book I am going to recommend is [Tidy time series forecasting with fable](https://tidyverts.github.io/tidy-forecasting-principles/). - The third book I am going to recommend is the book from my Advisor Prof. Bob Shumway & David Stoffer, Time Series Analysis and Its Applications [tsa4](https://www.stat.pitt.edu/stoffer/tsa4/). - [Introductory Time Series with R](https://www.springer.com/us/book/9780387886978) - [Time Series Analysis](https://www.springer.com/us/book/9780387759586). --- # R packages for Time Series Analsysis - fpp2 - [tsibble](https://pkg.earo.me/tsibble/) - [fable](https://github.com/tidyverts/fable) - [forecast](http://pkg.robjhyndman.com/forecast/) - [anomilize](https://business-science.github.io/anomalize/) - [prophet](https://facebook.github.io/prophet/) --- # Today To start we are going to draw many examples of time series showing: 0. white noise, independence 1. stationarity, nonstationarity 2. autocorrelation 3. cross correlation 4. naive forecasting 5. moving average 6. time series decomposition, trends, seasonal patterns, irregular patterns 7. exponential smoothing --- class: inverse, middle, center # Lets draw!!! --- class: center, middle # Thanks! Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan). The chakra comes from [remark.js](https://remarkjs.com), [**knitr**](http://yihui.name/knitr), and [R Markdown](https://rmarkdown.rstudio.com).