ts_fil_ema: Exponential Moving Average (EMA)

In tspredit: Time Series Prediction with Integrated Tuning

Exponential Moving Average (EMA)

Description

Smooth a series by exponentially decaying weights that give more importance to recent observations.

Usage

ts_fil_ema(ema = 3)

Arguments

ema	exponential moving average size

Details

EMA is related to simple exponential smoothing; it reacts faster to level changes than a simple moving average while reducing noise.

Value

A ts_fil_ema object.

References

• C. C. Holt (1957). Forecasting trends and seasonals by exponentially weighted moving averages. O.N.R. Research Memorandum.

Examples

# Exponential moving average smoothing on a noisy series
# Load package and example data
library(daltoolbox)
data(tsd)

# Inject an outlier to illustrate smoothing effect
tsd$y[9] <- 2 * tsd$y[9]

# Define EMA filter, fit and transform the series
filter <- ts_fil_ema(ema = 3)
filter <- fit(filter, tsd$y)
y <- transform(filter, tsd$y)

# Compare original vs smoothed series
plot_ts_pred(y = tsd$y, yadj = y)

tspredit documentation built on Feb. 11, 2026, 9:08 a.m.