Smoothing Transformation using Loess
smooth_vec() applies a LOESS transformation to a numeric vector.
smooth_vec(x, period = 30, span = NULL, degree = 2)
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 |
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 |
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). |
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.
A numeric vector
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()
library(tidyverse)
library(tidyquant)
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 = AVERAGE, .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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.