Chapter 19 Time series & longitudinal data
19.1 Time series analysis
A common theme in data analysis…comparing multiple points in time.
See also
19.1.1 Theory and methods in R
19.1.1.1 Rob J Hyndman
Rob Hyndman’s work in this area is second-to-none.
Start with his book with George Athanasopoulos, Forecasting: Principles and Practice (3rd edition) (Hyndman and Athanasopoulos 2021)
the cleverly named “Hyndsight” blog has a wealth of great articles on the topic, many of which explicitly reference the use of R.
“Software I’ve written” is a comprehensive list of the various packages that he has (co)authored
19.1.1.3 Rolling averages, exponential smoothing, etc
Steph Locke, Understanding Rolling Calculations in R
Exponential Smoothing Models, UC Business Analytics R Programming Guide, 2017-10-16
- Exponential Smoothing tutorial
19.1.2 {tsfeatures}
Methods for extracting various features from time series data
package
CRAN: tsfeatures: Time Series Feature Extraction
articles
19.1.3 {tsibble}
package
CRAN page: tsibble: Tidy Temporal Data Frames and Tools
github page: tsibble`: Tidy Temporal Data Frames and Tools
articles
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)
19.1.4 {padr}
package
CRAN page: padr: Quickly Get Datetime Data Ready for Analysis
articles
Andrew Clark, 2017-07-19, padr package example
19.1.5 {zoo}
package
CRAN page: zoo: S3 Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations)
19.2 Forecasting
Forecasting methods extrapolate past trends. There is a wealth of material supporting the theory and methods around this, much of it coming from econometrics.
19.2.1 Theory and methods
Kamala Kanta Mishra, Selecting Forecasting Methods in Data Science (2017-02-13)
19.2.2 R
Rob J Hyndman and George Athanasopoulos, Forecasting: Principles and Practice, 3rd edition
Kostiantyn Kravchuk, “Forecasting: Time Series Exploration Exercises (Part-1)” (2017-04-10)
19.2.3 Base R
A variety of time series analysis tools are included in base R. These include:
19.2.3.1 HoltWinters()
19.2.3.2 predict.HoltWinters()
- see Bart, 2012-07-16, Holt-Winters forecast using ggplot2
19.2.4 {fable}
“…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.”
package
documentation: fable
19.2.5 {prophet}
package
CRAN page: prophet: Automatic Forecasting Procedure
documentation: Prophet: forecasting at scale
articles
“Prophet: How Facebook operationalizes time series forecasting at scale” at Revolutions Analytics (2017-02-24)
19.3 Longitudinal data
19.3.1 {brolgar}
{brolgar} website – BRowse Over Longitudinal Data Graphically and Analytically in R
Nick Tierney, 2019-08-13, Explore longitudinal data with brolgar
19.4 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
19.4.1 Theory and methods
Statistics Canada, “Seasonal adjustment and trend-cycle estimation” (part of Statistics Canada Quality Guidelines, Catalogue 12-539-X)
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
19.4.2 R
19.4.2.1 {ggseas}
package
CRAN page: ggseas: ‘stats’ for Seasonal Adjustment on the Fly with ‘ggplot2’
articles
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.
19.4.2.2 {seasonal}
seasonal: R-interface to X-13ARIMA-SEATS
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 ggsdc)
package
CRAN page: seasonal: R Interface to X-13-ARIMA-SEATS’
github page: christophsax/seasonal
19.4.2.3 {x12}
package
articles
Rytis (2013-02-08) [Using X12-ARIMA with R](https://blogs.fsfe.org/rytis/2013/02/08/using-x12-arima-with-r/
19.4.2.4 {x13binary}
(US Census Bureau X-13, packaged for easy loading. Loads as a dependency for most of the other seasonal adjustment packages.)
package
CRAN page: x13binary: Provide the ‘x13ashtml’ Seasonal Adjustment Binary
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