---
title: "Intelligent Date & Time Sequences"
author: "Matt Dancho"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 2
vignette: >
%\VignetteIndexEntry{Intelligent Date & Time Sequences}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, echo = FALSE, message = FALSE, warning = FALSE}
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
fig.width = 8,
fig.height = 4.5,
fig.align = 'center',
out.width='95%',
dpi = 100
)
# 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.
```{r, message=FALSE}
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.
```{r}
# 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.
```{r}
# 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".
```{r}
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.
```{r}
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.
```{r}
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()`.
```{r}
tk_make_future_timeseries(idx, length_out = "2 years")
```
# Weekends & Holidays
__Make weekday sequence removing holidays__
- Result is 252 days.
```{r}
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
```{r}
tk_make_holiday_sequence("2012", calendar = "NYSE")
```
__Make future index removing holidays__
```{r}
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](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting). 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.