---
title: "Frequency and Trend Selection"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Frequency and Trend Selection}
%\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()
```
__Frequency and trend cycles__ are used in many time series applications including Seasonal ARIMA (SARIMA) forecasting and STL Decomposition. `timetk` includes functionality for __Automatic Frequency and Trend Selection.__ These tools use only the the timestamp information to make logical guesses about the frequency and trend.
# Prerequisites
Before we get started, load the following packages.
```{r, message = FALSE, warning=FALSE}
library(dplyr)
library(timetk)
```
# Data
__Daily Irregular Data__
The daily stock prices of Facebook from 2013 to 2016. Note that trading days only occur on "business days" (non-weekends and non-business-holidays).
```{r}
data(FANG)
FB_tbl <- FANG %>% dplyr::filter(symbol == "FB")
FB_tbl
```
__Sub-Daily Data__
Taylor's Energy Demand data at a 30-minute timestamp interval.
```{r}
taylor_30_min
```
# Applications
An example of where automatic frequency detection occurs is in the `plot_stl_diagnostics()` function.
```{r, fig.height=8}
taylor_30_min %>%
plot_stl_diagnostics(date, value,
.frequency = "auto", .trend = "auto",
.interactive = FALSE)
```
# Automatic Frequency & Trend Selection
## Specifying a Frequency or Trend
The `period` argument has three basic options for returning a frequency. Options include:
- "auto": A target frequency is determined using a pre-defined ___Time Scale Template___ (see below).
- time-based duration: (e.g. "7 days" or "2 quarters" per cycle)
- numeric number of observations: (e.g. 5 for 5 observations per cycle)
## Frequency
A _frequency_ is loosely defined as the number of observations that comprise a cycle in a data set.
Using `tk_get_frequency()`, we can pick a number of observations that will roughly define a frequency for the series.
__Daily Irregular Data__
Because `FB_tbl` is irregular (weekends and holidays are not present), the frequency selected is weekly but each week is only 5-days typically. So 5 is selected.
```{r, message = TRUE}
FB_tbl %>% tk_index() %>% tk_get_frequency(period = "auto")
```
__Sub-Daily Data__
This works as well for a sub-daily time series. Here we'll use `taylor_30_min` for a 30-minute timestamp series. The frequency selected is 48 because there are 48 timestamps (observations) in 1 day for the 30-minute cycle.
```{r}
taylor_30_min %>% tk_index() %>% tk_get_frequency("1 day")
```
## Trend
The trend is loosely defined as time span that can be aggregated across to visualize the central tendency of the data.
Using `tk_get_trend()`, we can pick a number of observations that will help describe a trend for the data.
__Daily Irregular Data__
Because `FB_tbl` is irregular (weekends and holidays are not present), the trend selected is 3 months but each week is only 5-days typically. So 64 observations is selected.
```{r, message = TRUE}
FB_tbl %>% tk_index() %>% tk_get_trend(period = "auto")
```
__Sub-Daily Data__
A 14-day (2 week) interval is selected for the "30-minute" interval data.
```{r}
taylor_30_min %>% tk_index() %>% tk_get_trend("auto")
```
# Time Scale Template
A ___Time-Scale Template___ is used to get and set the time scale template, which is used by `tk_get_frequency()` and `tk_get_trend()` when `period = "auto"`.
The predefined template is stored in a function `tk_time_scale_template()`. This is the default used by `timetk`.
__Accessing the Default Template__
You can access the current template with `get_tk_time_scale_template()`.
```{r}
get_tk_time_scale_template()
```
__Changing the Default Template__
You can modify the current template with `set_tk_time_scale_template()`.
# Learning More
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