DSF_ExponentialMA: Exponential Moving Average over a Data Stream

In mhahsler/stream: Infrastructure for Data Stream Mining

Exponential Moving Average over a Data Stream

Description

Applies an exponential moving average to components of a data stream.

Usage

DSF_ExponentialMA(dsd = NULL, dim = NULL, alpha = 0.5)

Arguments

dsd	The input stream as an DSD object.
dim	columns to which the filter should be applied. Default is all columns.
alpha	smoothing coefficient in [0, 1]. Larger means discounting older observations faster.

Details

The exponential moving average is calculated by:

S_t = \alpha Y_t + (1 - \alpha)\; S_{i-1}

with S_0 = Y_0.

Value

An object of class DSF_ExponentialMA (subclass of DSF and DSD).

Author(s)

Michael Hahsler

See Also

Other DSF: DSF(), DSF_Convolve(), DSF_Downsample(), DSF_FeatureSelection(), DSF_Func(), DSF_Scale(), DSF_dplyr()

Examples

# Smooth a time series
data(presidents)

stream <- data.frame(
    presidents,
    .time = time(presidents)) %>%
  DSD_Memory()

plot(stream, dim = 1, n = 120, method = "ts", main = "Original")

smoothStream <- stream %>% DSF_ExponentialMA(alpha = .7)
smoothStream

reset_stream(smoothStream)
plot(smoothStream, dim = 1, n = 120, method = "ts", main = "With ExponentialMA(.7)")

mhahsler/stream documentation built on June 15, 2025, 9:45 a.m.