# Frequency and Trend Selection

Source:`vignettes/TK06_Automatic_Frequency_And_Trend_Selection.Rmd`

`TK06_Automatic_Frequency_And_Trend_Selection.Rmd`

**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.

## Data

**Daily Irregular Data**

The daily stock prices of Facebook from 2013 to 2016 (courtesy of `tidyquant`

). Note that trading days only occur on “business days” (non-weekends and non-business-holidays).

```
## # A tibble: 1,008 × 8
## symbol date open high low close volume adjusted
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 FB 2013-01-02 27.4 28.2 27.4 28 69846400 28
## 2 FB 2013-01-03 27.9 28.5 27.6 27.8 63140600 27.8
## 3 FB 2013-01-04 28.0 28.9 27.8 28.8 72715400 28.8
## 4 FB 2013-01-07 28.7 29.8 28.6 29.4 83781800 29.4
## 5 FB 2013-01-08 29.5 29.6 28.9 29.1 45871300 29.1
## 6 FB 2013-01-09 29.7 30.6 29.5 30.6 104787700 30.6
## 7 FB 2013-01-10 30.6 31.5 30.3 31.3 95316400 31.3
## 8 FB 2013-01-11 31.3 32.0 31.1 31.7 89598000 31.7
## 9 FB 2013-01-14 32.1 32.2 30.6 31.0 98892800 31.0
## 10 FB 2013-01-15 30.6 31.7 29.9 30.1 173242600 30.1
## # … with 998 more rows
## # ℹ Use `print(n = ...)` to see more rows
```

**Sub-Daily Data**

Taylor’s Energy Demand data at a 30-minute timestamp interval.

`taylor_30_min`

```
## # A tibble: 4,032 × 2
## date value
## <dttm> <dbl>
## 1 2000-06-05 00:00:00 22262
## 2 2000-06-05 00:30:00 21756
## 3 2000-06-05 01:00:00 22247
## 4 2000-06-05 01:30:00 22759
## 5 2000-06-05 02:00:00 22549
## 6 2000-06-05 02:30:00 22313
## 7 2000-06-05 03:00:00 22128
## 8 2000-06-05 03:30:00 21860
## 9 2000-06-05 04:00:00 21751
## 10 2000-06-05 04:30:00 21336
## # … with 4,022 more rows
## # ℹ Use `print(n = ...)` to see more rows
```

## Applications

An example of where automatic frequency detection occurs is in the `plot_stl_diagnostics()`

function.

```
taylor_30_min %>%
plot_stl_diagnostics(date, value,
.frequency = "auto", .trend = "auto",
.interactive = FALSE)
```

`## frequency = 48 observations per 1 day`

`## trend = 672 observations per 14 days`

## 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
(see below).*Time Scale Template* - 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.

`FB_tbl %>% tk_index() %>% tk_get_frequency(period = "auto")`

`## frequency = 5 observations per 1 week`

`## [1] 5`

**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.

`taylor_30_min %>% tk_index() %>% tk_get_frequency("1 day")`

`## frequency = 48 observations per 1 day`

`## [1] 48`

### 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.

`FB_tbl %>% tk_index() %>% tk_get_trend(period = "auto")`

`## trend = 64 observations per 3 months`

`## [1] 64`

**Sub-Daily Data**

A 14-day (2 week) interval is selected for the “30-minute” interval data.

`taylor_30_min %>% tk_index() %>% tk_get_trend("auto")`

`## trend = 672 observations per 14 days`

`## [1] 672`

## 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()`

.

```
## # A tibble: 8 × 3
## time_scale frequency trend
## <chr> <chr> <chr>
## 1 second 1 hour 12 hours
## 2 minute 1 day 14 days
## 3 hour 1 day 1 month
## 4 day 1 week 3 months
## 5 week 1 quarter 1 year
## 6 month 1 year 5 years
## 7 quarter 1 year 10 years
## 8 year 5 years 30 years
```

**Changing the Default Template**

You can modify the current template with `set_tk_time_scale_template()`

.

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