psf: Forecasting of univariate time series using the PSF algorithm

In neerajdhanraj/PSF: Forecasting of univariate time series using the Pattern Sequence-based Forecasting (PSF) algorithm

Description

Takes an univariate time series and the prediction horizon as inputs.

Usage

psf(data, n.ahead, 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).
n.ahead	The number of predicted values to be obtained.
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

A list with 3 elements:

predictions	Vector with the resulting predictions
k	Number of clusters used
w	Window size used

Examples

## Forecast the next 12 values of the univariate time series: nottem (package:datasets).
psf(nottem, 12)

## Forecast the next 48 values of the univariate time series: sunspots (package:datasets).
psf(sunspots, 48)

neerajdhanraj/PSF documentation built on May 23, 2019, 1:31 p.m.