sparse: sparse

In cugliari/enercast: Electrical energy forecast tools

Description

The predictive model is based on a "colinearity" argument between one day and the other Each traning sample consists of two consecutive days (Ltoday,Ltomorrow). A dictionary is then learnt to represent a set of training samples with that form. The dictionary will then be an ensemble of two-day atoms, the upper part corresponding to "todays", and the lower part to "tomorrows", [Dtod^T Dtom^T]^T To predict tomorrow's load, w represent today's load Ltod in terms of Dtod and obtain a set of coefficients a Whe then estimate Ltom as Dtom*a. The underlying hypothesis is that "a" would be essentially the same if we had asked to represent the whole vector [Ltod,Ltom]

Usage

sparse(train.data, natoms, lambda, delta = 24)

Arguments

train.data	an NxM matrix where each column is an hour by hour variable (load, temperature, etc.)
natoms	normalization mode
lambda	number of atoms in target dictionary
delta	penalty term to be used

Value

a list with dictionaries

cugliari/enercast documentation built on Sept. 15, 2019, 10:13 a.m.