Calendar Features

    # message = FALSE,
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    fig.height = 4.5,
    fig.align = 'center',
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# devtools::load_all() # Travis CI fails on load_all()

This vignette covers making and working with Calendar Features, which are derived from a time series index, or the sequence of date/datetime stamps that accompany time series data.


The time series index consists of a collection of time-based values that define when each observation occurred, is the most important part of a time series object.

The index gives the user a lot of information in a simple timestamp. Consider the datetime "2016-01-01 00:00:00".

From this timestamp, we can decompose the date and time information to get the signature, which consists of the year, quarter, month, day, day of year, day of month, hour, minute, and second of the occurrence of a single observation. Further, the difference between two or more observations is the frequency from which we can obtain even more information such as the periodicity of the data and whether or not these observations are on a regular interval. This information is critical as it provides the basis for performance over time in finance, decay rates in biology, growth rates in economics, and so on.

In this vignette the user will be exposed to:

  1. Time Series Index
  2. Time Series Signature
  3. Time Series Summary


Before we get started, load the following packages.



We'll use the Facebook stock prices from the FANG data set. These are the historical stock prices (open, high, low, close, volume, and adjusted) for the "FB" stock from 2013 through 2016.


FB_tbl <- FANG %>% dplyr::filter(symbol == "FB")

To simplify the tutorial, we will select only the "date" and "volume" columns. For the FB_vol_date data frame, we can see from the "date" column that the observations are daily beginning on the second day of 2013.

FB_vol_date <- FB_tbl %>% select(date, volume)

Time Series Index

Before we can analyze an index, we need to extract it from the object. The function tk_index() extracts the index from any time series object including data frame (or tbl), xts, zoo, etc. The index is always returned in the native date, datetime, yearmon, or yearqtr format. Note that the index must be in one of these time-based classes for extraction to work:

Extract the index using tk_index(). The structure is shown to see the output format, which is a vector of dates.

# idx_date
idx_date <- tk_index(FB_vol_date)

Time Series Signature

The index can be decomposed into a signature. The time series signature is a unique set of properties of the time series values that describe the time series.

Get Functions - Turning an Index into Information

The function tk_get_timeseries_signature() can be used to convert the index to a tibble containing the following values (columns):

# idx_date signature

Augment Functions (Adding Many Features to a Data Frame)

It's usually important to keep the index signature with the values (e.g. volume in our example). We can use an expedited approach with tk_augment_timeseries_signature(), which adds the signature to the end of the time series object.

# Augmenting a data frame
FB_vol_date_signature <- FB_vol_date %>% tk_augment_timeseries_signature(.date_var = date)

Modeling is now much easier. As an example, we can use linear regression model using the lm() function with the month and year as a predictor of volume.

# Example Benefit 2: Modeling is easier
fit <- lm(volume ~ year + month.lbl, data = FB_vol_date_signature)

Time Series Summary

The next index analysis tool is the summary metrics, which can be retrieved using the tk_get_timeseries_summary() function. The summary reports the following attributes as a single-row tibble.

General Summary:

The first six columns are general summary information.

# idx_date: First six columns, general summary

Differences Summary:

The next group of values are the differences summary (i.e. summary of frequency). All values are in seconds:

# idx_date: Last six columns, difference summary

The differences provide information about the regularity of the frequency. Generally speaking if all difference values are equal, the index is regular. However, scales beyond "day" are never theoretically regular since the differences in seconds are not equivalent. However, conceptually monthly, quarterly and yearly data can be thought of as regular if the index contains consecutive months, quarters, or years, respectively. Therefore, the difference attributes are most meaningful for daily and lower time scales because the difference summary always indicates level of regularity.

From the second group (differences summary), we immediately recognize that the mean is different than the median and therefore the index is irregular (meaning certain days are missing). Further we can see that the maximum difference is 345,600 seconds, indicating the maximum difference is 4 days (345,600 seconds / 86400 seconds/day).

Learning More

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timetk documentation built on Nov. 2, 2023, 6:18 p.m.