In business-science/timekit: A Tool Kit for Working with Time Series

knitr::opts_chunk$set(
    message = FALSE,
    warning = FALSE,
    fig.width = 8, 
    fig.height = 4.5,
    fig.align = 'center',
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# devtools::load_all() # Travis CI fails on load_all()

Creating and modifying date sequences is critical to machine learning projects. We discuss:

Making a Time Series Sequence: tk_make_timeseries()
Making a Future Sequence: tk_make_future_timeseries()
Holiday & Weekday/Weekend Sequences

Prerequisites

Before we get started, load the following packages.

library(dplyr)
library(timetk)

Making a Time Series Sequence

tk_make_timeseries() improves on the seq.Date() and seq.POSIXt() functions by simplifying into 1 function. Intelligently handles character dates and logical assumptions based on user inputs.

By Day

Can use by = "day" or leave blank.
include_endpoints = FALSE removes the last value so your series is only 7 observations.

# Selects by day automatically
tk_make_timeseries("2011", length_out = "7 days", include_endpoints = FALSE)

By 2 Seconds

Can use by = "2 sec" to adjust the interval width.
include_endpoints = TRUE keeps the last value the series ends on the 6th second.

# Guesses by second
tk_make_timeseries("2016", by = "2 sec", length_out = "6 seconds")

Length Out = 1 year 6 months

length_out = "1 year 6 months" - Can include complex expressions like "1 year 4 months 6 days".

tk_make_timeseries("2012-07", 
                   by = "1 month",
                   length_out = "1 year 6 months", 
                   include_endpoints = FALSE)

Go In Reverse

To go in reverse, just use end_date as where you want the series to end.

tk_make_timeseries(end_date = "2012-07-01", 
                   by = "1 month",
                   length_out = "1 year 6 months")

Future Time Series Sequence

A common operation is to make a future time series sequence that mimics an existing. This is what tk_make_future_timeseries() is for.

Suppose we have an existing time index.

idx <- tk_make_timeseries("2012", by = "3 months", 
                          length_out = "2 years", 
                          include_endpoints = FALSE)
idx

Make a Future Time Series from an Existing

We can create a future time sequence from the existing sequence using tk_make_future_timeseries().

tk_make_future_timeseries(idx, length_out = "2 years")

Weekends & Holidays

Make weekday sequence removing holidays

Result is 252 days.

idx <- tk_make_weekday_sequence("2012",
                                remove_weekends = TRUE, 
                                remove_holidays = TRUE, calendar = "NYSE")

tk_get_timeseries_summary(idx)

Which holidays were removed?

NYSE Trading holidays which are days most businesses observe

tk_make_holiday_sequence("2012", calendar = "NYSE")

Make future index removing holidays

holidays <- tk_make_holiday_sequence(
    start_date = "2013-01-01",
    end_date   = "2013-12-31",
    calendar   = "NYSE")

idx_future <- idx %>%
   tk_make_future_timeseries(length_out       = "1 year",
                             inspect_weekdays = TRUE,
                             skip_values      = holidays)

tk_get_timeseries_summary(idx_future)

Learning More

My Talk on High-Performance Time Series Forecasting

Time series is changing. Businesses now need 10,000+ time series forecasts every day.

High-Performance Forecasting Systems will save companies MILLIONS of dollars. Imagine what will happen to your career if you can provide your organization a "High-Performance Time Series Forecasting System" (HPTSF System).

I teach how to build a HPTFS System in my High-Performance Time Series Forecasting Course. If interested in learning Scalable High-Performance Forecasting Strategies then take my course. You will learn:

Time Series Machine Learning (cutting-edge) with Modeltime - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
NEW - Deep Learning with GluonTS (Competition Winners)
Time Series Preprocessing, Noise Reduction, & Anomaly Detection
Feature engineering using lagged variables & external regressors
Hyperparameter Tuning
Time series cross-validation
Ensembling Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
Scalable Forecasting - Forecast 1000+ time series in parallel
and more.