psf: Train a PSF model from an univariate time series using the...

In PSF: Forecasting of Univariate Time Series Using the Pattern Sequence-Based Forecasting (PSF) Algorithm

Train a PSF model from an univariate time series using the PSF algorithm

Description

Takes an univariate time series as input. Optionally, specific internal parameters of the PSF algorithm can be also specified.

Usage

psf(data, k = seq(2, 10), w = seq(1, 10), cycle = 24)

Arguments

data	Input univariate time series, in any format (time series (ts), vector, matrix, list, data frame).
k	The number of clusters, or a vector of candidate values to search for the optimum automatically.
w	The window size, or a vector of candidate values to search for the optimum automatically.
cycle	The number of values that conform a cycle in the time series (e.g. 24 hours per day). Only used when input data is not in time series format.

Value

An object of class 'psf' with 7 elements:

original_series	Original time series stored to be used internally to build further plots.
train_data	Adapted and normalized internal time series used to train the PSF model.
k	Number of clusters used
w	Window size used
cycle	Determined cycle for the input time series.
dmin	Minimum value of the input time series (used to denormalize internally further predictions).
dmax	Maximum value of the input time series (used to denormalize internally further predictions).

Examples

## Train a PSF model from the univariate time series 'nottem' (package:datasets).
p <- psf(nottem)

## Train a PSF model from the univariate time series 'sunspots' (package:datasets).
p <- psf(sunspots)

PSF documentation built on May 1, 2022, 5:07 p.m.