3,608 research outputs found
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices
Recurrent neural networks (RNNs) with rich feature vectors of past values can
provide accurate point forecasts for series that exhibit complex serial
dependence. We propose two approaches to constructing deep time series
probabilistic models based on a variant of RNN called an echo state network
(ESN). The first is where the output layer of the ESN has stochastic
disturbances and a shrinkage prior for additional regularization. The second
approach employs the implicit copula of an ESN with Gaussian disturbances,
which is a deep copula process on the feature space. Combining this copula with
a non-parametrically estimated marginal distribution produces a deep
distributional time series model. The resulting probabilistic forecasts are
deep functions of the feature vector and also marginally calibrated. In both
approaches, Bayesian Markov chain Monte Carlo methods are used to estimate the
models and compute forecasts. The proposed deep time series models are suitable
for the complex task of forecasting intraday electricity prices. Using data
from the Australian National Electricity Market, we show that our models
provide accurate probabilistic price forecasts. Moreover, the models provide a
flexible framework for incorporating probabilistic forecasts of electricity
demand as additional features. We demonstrate that doing so in the deep
distributional time series model in particular, increases price forecast
accuracy substantially
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to
predict real valued time-series. The method consists in projecting the output
of the internal layer of the network on a space with lower dimensionality,
before training the output layer to learn the target task. Notably, we enforce
a regularization constraint that leads to better generalization capabilities.
We evaluate the performances of our approach on several benchmark tests, using
different techniques to train the readout of the network, achieving superior
predictive performance when using the proposed framework. Finally, we provide
an insight on the effectiveness of the implemented mechanics through a
visualization of the trajectory in the phase space and relying on the
methodologies of nonlinear time-series analysis. By applying our method on well
known chaotic systems, we provide evidence that the lower dimensional embedding
retains the dynamical properties of the underlying system better than the
full-dimensional internal states of the network
Local Short Term Electricity Load Forecasting: Automatic Approaches
Short-Term Load Forecasting (STLF) is a fundamental component in the
efficient management of power systems, which has been studied intensively over
the past 50 years. The emerging development of smart grid technologies is
posing new challenges as well as opportunities to STLF. Load data, collected at
higher geographical granularity and frequency through thousands of smart
meters, allows us to build a more accurate local load forecasting model, which
is essential for local optimization of power load through demand side
management. With this paper, we show how several existing approaches for STLF
are not applicable on local load forecasting, either because of long training
time, unstable optimization process, or sensitivity to hyper-parameters.
Accordingly, we select five models suitable for local STFL, which can be
trained on different time-series with limited intervention from the user. The
experiment, which consists of 40 time-series collected at different locations
and aggregation levels, revealed that yearly pattern and temperature
information are only useful for high aggregation level STLF. On local STLF
task, the modified version of double seasonal Holt-Winter proposed in this
paper performs relatively well with only 3 months of training data, compared to
more complex methods
Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data
Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used
in the machine learning and dynamical systems literature to represent complex
dynamical or sequential relationships between variables. More recently, as deep
learning models have become more common, RNNs have been used to forecast
increasingly complicated systems. Dynamical spatio-temporal processes represent
a class of complex systems that can potentially benefit from these types of
models. Although the RNN literature is expansive and highly developed,
uncertainty quantification is often ignored. Even when considered, the
uncertainty is generally quantified without the use of a rigorous framework,
such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a
more formal framework while maintaining the forecast accuracy that makes these
models appealing, by presenting a Bayesian RNN model for nonlinear
spatio-temporal forecasting. Additionally, we make simple modifications to the
basic RNN to help accommodate the unique nature of nonlinear spatio-temporal
data. The proposed model is applied to a Lorenz simulation and two real-world
nonlinear spatio-temporal forecasting applications
Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network
In this study, an artificial neural network (ANN) based on particle swarm
optimization (PSO) was developed for the time series prediction. The hybrid
ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the
short-term . The performance prediction was evaluated and compared with
another studies available in the literature. Also, we presented properties of
the dynamical system via the study of chaotic behaviour obtained from the
predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with
a Gaussian stochastic procedure (called {\it stochastic} hybrid ANN+PSO) in
order to obtain a new estimator of the predictions, which also allowed us to
compute uncertainties of predictions for noisy Mackey--Glass chaotic time
series. Thus, we studied the impact of noise for several cases with a white
noise level () from 0.01 to 0.1.Comment: 11 pages, 8 figure
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