`smooth_vec()`

applies a LOESS transformation to a numeric vector.

## Arguments

- x
A numeric vector to have a smoothing transformation applied.

- period
The number of periods to include in the local smoothing. Similar to window size for a moving average. See details for an explanation

`period`

vs`span`

specification.- span
The span is a percentage of data to be included in the smoothing window. Period is preferred for shorter windows to fix the window size. See details for an explanation

`period`

vs`span`

specification.- degree
The degree of the polynomials to be used. Accetable values (least to most flexible): 0, 1, 2. Set to 2 by default for 2nd order polynomial (most flexible).

## Details

**Benefits:**

When using

`period`

, the effect is**similar to a moving average without creating missing values.**When using

`span`

, the effect is to detect the trend in a series**using a percentage of the total number of observations.**

**Loess Smoother Algorithm**
This function is a simplified wrapper for the `stats::loess()`

with a modification to set a fixed `period`

rather than a percentage of
data points via a `span`

.

**Why Period vs Span?**
The `period`

is fixed whereas the `span`

changes as the number of observations change.

**When to use Period?**
The effect of using a `period`

is similar to a Moving Average where the Window Size
is the * Fixed Period*. This helps when you are trying to smooth local trends.
If you want a 30-day moving average, specify

`period = 30`

.**When to use Span?**
Span is easier to specify when you want a * Long-Term Trendline* where the
window size is unknown. You can specify

`span = 0.75`

to locally regress
using a window of 75% of the data.## See also

Loess Modeling Functions:

`step_smooth()`

- Recipe for`tidymodels`

workflow

Additional Vector Functions:

Box Cox Transformation:

`box_cox_vec()`

Lag Transformation:

`lag_vec()`

Differencing Transformation:

`diff_vec()`

Rolling Window Transformation:

`slidify_vec()`

Loess Smoothing Transformation:

`smooth_vec()`

Fourier Series:

`fourier_vec()`

Missing Value Imputation for Time Series:

`ts_impute_vec()`

## Examples

```
library(dplyr)
library(ggplot2)
library(timetk)
# Training Data
FB_tbl <- FANG %>%
filter(symbol == "FB") %>%
select(symbol, date, adjusted)
# ---- PERIOD ----
FB_tbl %>%
mutate(adjusted_30 = smooth_vec(adjusted, period = 30, degree = 2)) %>%
ggplot(aes(date, adjusted)) +
geom_line() +
geom_line(aes(y = adjusted_30), color = "red")
# ---- SPAN ----
FB_tbl %>%
mutate(adjusted_30 = smooth_vec(adjusted, span = 0.75, degree = 2)) %>%
ggplot(aes(date, adjusted)) +
geom_line() +
geom_line(aes(y = adjusted_30), color = "red")
# ---- Loess vs Moving Average ----
# - Loess: Using `degree = 0` to make less flexible. Comperable to a moving average.
FB_tbl %>%
mutate(
adjusted_loess_30 = smooth_vec(adjusted, period = 30, degree = 0),
adjusted_ma_30 = slidify_vec(adjusted, .period = 30,
.f = mean, .partial = TRUE)
) %>%
ggplot(aes(date, adjusted)) +
geom_line() +
geom_line(aes(y = adjusted_loess_30), color = "red") +
geom_line(aes(y = adjusted_ma_30), color = "blue") +
labs(title = "Loess vs Moving Average")
```