796 research outputs found
GEFCOM 2014 - Probabilistic Electricity Price Forecasting
Energy price forecasting is a relevant yet hard task in the field of
multi-step time series forecasting. In this paper we compare a well-known and
established method, ARMA with exogenous variables with a relatively new
technique Gradient Boosting Regression. The method was tested on data from
Global Energy Forecasting Competition 2014 with a year long rolling window
forecast. The results from the experiment reveal that a multi-model approach is
significantly better performing in terms of error metrics. Gradient Boosting
can deal with seasonality and auto-correlation out-of-the box and achieve lower
rate of normalized mean absolute error on real-world data.Comment: 10 pages, 5 figures, KES-IDT 2015 conference. The final publication
is available at Springer via http://dx.doi.org/10.1007/978-3-319-19857-6_
Solar Irradiance Forecasting Using Dynamic Ensemble Selection
Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics
Kalman Filter Bibliography: Agriculture, Biology, and Medicine
27 pages, 1 article*Kalman Filter Bibliography: Agriculture, Biology, and Medicine* (Federer, Walter T.; Murty, B. R.) 27 page
Multi-Factor Gegenbauer Processes and European Inflation Rates
In this paper we specify a multi-factor long-memory process that enables us to estimate the fractional differencing parameters at each frequency separately, and adopt this framework to model quarterly prices in three European countries (France, Italy and the UK). The empirical results suggest that inflation in France and Italy is nonstationary. However, while for the former country this applies both to the zero and the seasonal frequencies, in the case of Italy the nonstationarity comes exclusively from the long-run or zero frequency. In the UK, inflation seems to be stationary with a component of long memory at both the zero and the semi-annual frequencies, especially at the former.fractional integration, long memory, inflation
Surrogate time series
Before we apply nonlinear techniques, for example those inspired by chaos
theory, to dynamical phenomena occurring in nature, it is necessary to first
ask if the use of such advanced techniques is justified "by the data". While
many processes in nature seem very unlikely a priori to be linear, the possible
nonlinear nature might not be evident in specific aspects of their dynamics.
The method of surrogate data has become a very popular tool to address such a
question. However, while it was meant to provide a statistically rigorous,
foolproof framework, some limitations and caveats have shown up in its
practical use. In this paper, recent efforts to understand the caveats, avoid
the pitfalls, and to overcome some of the limitations, are reviewed and
augmented by new material. In particular, we will discuss specific as well as
more general approaches to constrained randomisation, providing a full range of
examples. New algorithms will be introduced for unevenly sampled and
multivariate data and for surrogate spike trains. The main limitation, which
lies in the interpretability of the test results, will be illustrated through
instructive case studies. We will also discuss some implementational aspects of
the realisation of these methods in the TISEAN
(http://www.mpipks-dresden.mpg.de/~tisean) software package.Comment: 28 pages, 23 figures, software at
http://www.mpipks-dresden.mpg.de/~tisea
Time series in forecasting and decision: an experiment in elman nn models
The paper examines the role of analytical tools in analysis of economic statistical data (commonly referred to as econometry) and artificial neural network (ANN) models for time series processing in forecasting, decision and control. The emphasis is put on the comparative analysis of classical econometric approach of pattern recognition (Box-Jenkins approach) and neural network models, especially the class of recurrent ones and Elman ANN in particular. A comprehensive experiment in applying the latter modeling has been carried out, some specific applications software developed, and a number of benchmark series from the literature processed. This paper reports on comparison findings in favor of Elman ANN modeling, and on the use of a designed program package that encompasses routines for regression, ARIMA and ANN analysis of time series. The analysis is illustrated by two sample examples known as difficult to model via any technique
Does money matter in inflation forecasting?
This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.Forecasting ; Inflation (Finance) ; Monetary theory
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