How to Visualize Time Series Data: Tidy Forecasting in R
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Interested in more time series tutorials? Learn more R tips:
 Time Series Machine Learning (and Feature Engineering) in R, and
 Time Series in 5Minutes, Part 6: Modeling Time Series Data.
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Plot time series data using the fpp2
, fpp3
, and timetk
forecasting frameworks.
1. Set Up
1.1. Introduction
There are a number of forecasting packages written in R to choose from, each with their own pros and cons.
For almost a decade, the forecast
package has been a rocksolid framework for time series forecasting. However, within the last year or so an official updated version has been released named fable
which now follows tidy methods as opposed to base R.
More recently, modeltime
has been released and this also follows tidy methods. However, it is strictly used for modeling. For data manipulation and visualization, the timetk package will be used which is written by the same author as modeltime
.
The following is a code comparison of various time series visualizations between these frameworks: fpp2
, fpp3
and timetk
.
A few things to keep in mind:
 Only the essential code has been provided
 Nonessential code such as plot titles and themes has been excluded
 All plots utilize the Business Science ggplot theme
1.2 Load Libraries
2. TS vs tsibble
 The base ts object is used by
forecast
&fpp2
 The special tsibble object is used by
fable
&fpp3
 The standard tibble object is used by
timetk
&modeltime
2.1 Load Time Series Data
For the next few visualizations, we will utilize a dataset containing quarterly production values of certain commodities in Australia.
Always check the class of your time series data.
2.2 fpp2 Method: From tibble to ts
2.3 fpp3 Method: From ts to tsibble
2.3.1 Pivot Wide
2.3.2 Pivot Long
2.4 timetk Method: From tsibble/ts to tibble
2.4.1 Pivot Wide
Workaround for indexing issue with tsibble and R 4.0 and up.
2.4.2 Pivot Long
3. Time Series Plots
When analyzing time series plots, look for the following patterns:

Trend: A longterm increase or decrease in the data; a “changing direction”.

Seasonality: A seasonal pattern of a fixed and known period. If the frequency is unchanging and associated with some aspect of the calendar, then the pattern is seasonal.

Cycle: A rise and fall pattern not of a fixed frequency. If the fluctuations are not of a fixed frequency then they are cyclic.

Seasonal vs Cyclic: Cyclic patterns are longer and more variable than seasonal patterns in general.
3.1 fpp2 Method: Plot Multiple Series On Same Axes
3.2 fpp3 Method: Plot Multiple Series On Same Axes
3.3 ggplot Method: Plot Multiple Series On Same Axes
Note that plotting multiple plots on the same axes has not been implemented into timetk
. Use ggplot
.
3.4 fpp2 Method: Plot Multiple Series On Separate Axes
Facetted plot with fpp2
3.5 fpp3 Method: Plot Multiple Series On Separate Axes
Facetted plot with fpp3
3.6 timetk Method: Plot Multiple Series On Separate Axes
Facetted plot with timetk
4. Seasonal Plots
Use seasonal plots for identifying time periods in which the patterns change.
4.1 fpp2 Method: Plot Individual Seasons
Seasonal plot with fpp2
4.2 fpp3 Method: Plot Individual Seasons
Seasonal plot with fpp3
4.3 ggplot Method: Plot Individual Seasons
Note that seasonal plots have not been implemented into timetk
. Use ggplot to write:
Seasonal plot with ggplot
5. Subseries Plots
Use subseries plots to view seasonal changes over time.
5.1 fpp2 Method: Plot Subseries on Same Axes
Subseries plots on the same axes using fpp2
5.2 fpp3 Method: Plot Subseries on Separate Axes
Subseries plots on the same axes using fpp3
5.3 timetk Method: Plot Subseries on Separate Axes
Subseries plots on the same axes using timetk
6. Lag Plots
Use lag plots to check for randomness.
6.1 fpp2 Method: Plot Multiple Lags
Lag plots using fpp2
6.2 fpp3 Method: Plot Multiple Lags
Lag plots using fpp3
6.3 timetk Method (Hack?) : Plot Multiple Lags
Now you can plot value vs lag_value
Lag plots using timetk
7. Autocorrelation Function Plots
The autocorrelation function measures the linear relationship between lagged values of a time series. The partial autocorrelation function measures the linear relationship between the correlations of the residuals.
ACF
 Visualizes how much the most recent value of the series is correlated with past values of the series (lags)
 If the data has a trend, then the autocorrelations for small lags tend to be positive and large because observations nearby in time are also nearby in size
 If the data are seasonal, then the autocorrelations will be larger for seasonal lags at multiples of seasonal frequency than other lags
PACF
 Visualizes whether certain lags are good for modeling or not; useful for data with a seasonal pattern
 Removes dependence of lags on other lags by using the correlations of the residuals
7.1 fpp2 Method: Plot ACF + PACF
Are autocorrelations large at seasonal lags? Are the most recent lags above the white noise threshold?
7.2 fpp3 Method: Plot ACF + PACF
The autocorrelations are not large at seasonal lags so this series is nonseasonal. The most recent lags show that there is a trend.
7.3 timetk Method: Plot ACF & PACF
ACF shows more recent lags are above the white noise significance bars denoting a trend. PACF shows that including lag 1 would be good for modeling purposes.
8. Summary
As with all things in life, there are good and bad sides to using any of these three forecasting frameworks for visualizing time series. All three have similar functionality as it relates to visualizations.
8.1 fpp2
 Code requires minimal parameters
 Uses basets format
 Uses ggplot for visualizations
 Mostly incompatible with tidyverse for data manipulation
 No longer maintained except for bug fixes
8.2 fpp3
 Code requires minimal parameters
 Uses proprietary tsibble format with special indexing tools
 Uses ggplot for visualizations
 Mostly compatible with tidyverse for data manipulation; tsibble may cause issues
 Currently maintained
8.3 timetk
 Code requires multiple parameters but provides more granularity
 Uses standard tibble format
 Uses ggplot and plotly for visualizations
 Fully compatible with tidyverse for data manipulation
 Currently maintained
Author: Joon Im
Joon is a data scientist with both R and Python with an emphasis on forecasting techniques  LinkedIn.
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