Time Series API Reference

EchoStateNetwork(X, Y, Reservoir = 1000;
                    L2 = 1e-8, alpha = 0.25, SpectralRadius = 1.00, Sparsity = 0.99, Noise = -1.00,
                    bias = true, burnin = 0)

Creates an echo state network model/object.

RollingWindow(samples::Int,windowsize::Int,skip::Int)

Creates a RollingWindow iterator from a number of samples and a static windowsize where every iteration skip steps are skipped. The iterator can be used in for loops to iteratively return indices of a dynamic rolling window.

RollingWindow(samples::Int,windowsize::Int)

Creates a RollingWindow iterator from a number of samples and a static windowsize. The iterator can be used in for loops to iteratively return indices of a dynamic rolling window.

SimpleAverage( univariate::Array )

SimpleAverage( univariate )

(sa::SimpleAverage)()

Predicts with a naive forecaster model.

EWMA(Initial::Float64, Lambda::Float64) = ewma(Lambda, Initial, Initial, RunningVar(Initial))

Constructs an exponentially weighted moving average object from an vector of scalar property values Initial and the decay parameter Lambda. This computes the running statistcs neccesary for creating the EWMA model using the interval provided and updates the center value to the mean of the provided values.

EWMA(Initial::Float64, Lambda::Float64) = ewma(Lambda, Initial, Initial, RunningVar(Initial))

Constructs an exponentially weighted moving average object from an initial scalar property value Initial and the decay parameter Lambda. This defaults the center value to be the initial value.

Limits(P::ewma; k = 3.0)

This function returns the upper and lower control limits with a k span of variance for an EWMA object P.

NaiveForecaster( univariate::Array )

Makes a forecaster model that simply predicts the last value it has seen for all future values.

PredictFn(model::EchoStateNetwork, X; UseLastState = true, burnin = 0)

This function uses X data to predict Y values from a stored ESN model.
Burnin can be used here, but it simply rejects the first `N` samples from storage/allocation.
UseLastState will employ the last known state from the training set as the first state estimate (reccomended)

TuneRidge(Y, model::EchoStateNetwork, L2, burnin = 0 )

This function modifies an ESN model's output weights without reconstructing the dynamic resevoir.
Basically this is a time saver!

Update( model::naiveforecaster, newdata::Float64 )

Variance(P::ewma)

This function returns the EWMA control variance.

update!( model::NaiveForecaster, newdata::Float64 )

Makes a forecast model that simply predicts the last value it has seen for all future values.

update( model::NaiveForecaster, newdata::Float64 )

returns an updated forecast model that simply predicts the last value it has seen for all future values.

(nf::NaiveForecaster)()

Predicts with a naive forecaster model.

EWMA(P::ewma)(New; train = true)

Provides an EWMA score for a New scalar value. If train == true the model is updated to include this new value.

ChangeCenter(P::ewma, new::Float64)

This is a convenience function to update the center of a P EWMA model, to a new scalar value.