Chapter 18 Time series & longitudinal data
18.1 Time series analysis
A common theme in data analysis…comparing multiple points in time.
18.1.1 Theory and methods in R
220.127.116.11 Rob J Hyndman
Rob Hyndman’s work in this area is second-to-none.
Methods for extracting various features from time series data
CRAN page: tsibble: Tidy Temporal Data Frames and Tools
github page: tsibble`: Tidy Temporal Data Frames and Tools
Earo Wang, 2018-12-20, “Reintroducing tsibble: data tools that melt the clock”
Earo Wang and Dianne Cook and Rob J Hyndman, January 2019, “A new tidy data structure to support exploration and modeling of temporal data”(Wang, Cook, and Hyndman 2019)
Andrew Clark, 2017-07-19, padr package example
Forecasting methods extrapolate past trends. There is a wealth of material supporting the theory and methods around this, much of it coming from econometrics.
18.2.1 Theory and methods
Kamala Kanta Mishra, Selecting Forecasting Methods in Data Science (2017-02-13)
Rob J Hyndman and George Athanasopoulos, Forecasting: Principles and Practice, 3rd edition
Kostiantyn Kravchuk, “Forecasting: Time Series Exploration Exercises (Part-1)” (2017-04-10)
18.2.3 Base R
A variety of time series analysis tools are included in base R. These include:
“…provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. Data, model and forecast objects are all stored in a tidy format.”
CRAN page: prophet: Automatic Forecasting Procedure
documentation: Prophet: forecasting at scale
“Prophet: How Facebook operationalizes time series forecasting at scale” at Revolutions Analytics (2017-02-24)
18.3 Longitudinal data
18.4 Seasonal adjustment
From the wikipedia entry:
Seasonal adjustment is a statistical method for removing the seasonal component of a time series that exhibits a seasonal pattern. It is usually done when wanting to analyse the trend of a time series independently of the seasonal components. It is normal to report seasonally adjusted data for unemployment rates to reveal the underlying trends in labor markets. Many economic phenomena have seasonal cycles, such as agricultural production and consumer consumption, e.g. greater consumption leading up to Christmas. It is necessary to adjust for this component in order to understand what underlying trends are in the economy and so official statistics are often adjusted to remove seasonal components.
Seasonal adjustment: wikipedia entry, 2016-05-07
18.4.1 Theory and methods
Statistics Canada, Statistical Methodology Research and Development Program Achievements, 2017/2018 2.2 Time Series Research and Analysis Centre (TSRAC)
Feldpausch, Roxanne M.; Hood, Catherine C. (2003) “Some properties of the seasonal adjustment diagnostics in X-12-ARIMA”, Statistics Canada International Symposium Series: Proceedings, Issue 2003001.
U.S. Census Bureau, The X-13ARIMA-SEATS Seasonal Adjustment Program
Ellis, Peter. 2016-10-12. “Update of
ggseas for seasonal decomposition on the fly”, blog entry
Ellis, Peter. 2016-03-28. “Seasonal decomposition in the ggplot2 universe with ggseas”, blog entry.
Ellis, Peter. 2016-02-08. “ggseas package for seasonal adjustment on the fly with ggplot2”, blog entry.
Packages the U.S. Census Bureau’s gold-standard X13-SEATS-ARIMA for use in R.
“…the best interface on the planet to the X13-SEATS-ARIMA time series analysis application from the US Census Department, which is the industry standard particularly for official statistics agencies doing seasonal adjustment.” (Peter Ellis, vignette for
CRAN page: seasonal: R Interface to X-13-ARIMA-SEATS’
github page: christophsax/seasonal
Rytis (2013-02-08) [Using X12-ARIMA with R](https://blogs.fsfe.org/rytis/2013/02/08/using-x12-arima-with-r/
(US Census Bureau X-13, packaged for easy loading. Loads as a dependency for most of the other seasonal adjustment packages.)
Wang, Earo, Dianne Cook, and Rob J Hyndman. 2019. “A New Tidy Data Structure to Support Exploration and Modeling of Temporal Data.”