A Bayesian entropy approach to forecasting

This thesis describes a new approach to steady-state forecasting models based on Bayesian principles and Information Theory. Shannon's entropy function and Jaynes' principle of maximum entropy are the essen­tial results borrowed from Information Theory and are extensively used in the model formulation. The Bayesian Entropy Forecasting (BEF) models obtained in this way extend beyond the constraints of normality and linearity required in all existing forecasting methods. In this sense, it reduces in the normal case to the well known Harrison and Stevens steady-state model. Examples of such models are presented, including the Poisson-gamma process, the Binomial-Beta process and the Truncated Normal process. For all of these, numerical applications using real and simulated data are shown, including further analyses of epidemic data of Cliff et al, (1975)

Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features

An important aspect of empirical research based on the vector autoregressive (VAR) model is the choice of the lag order, since all inference in the VAR model depends on the correct model specification. Literature has shown important studies of how to select the lag order of a nonstationary VAR model subject to cointegration restrictions. In this work, we consider an additional weak form (WF) restriction of common cyclical features in the model in order to analyze the appropriate way to select the correct lag order. Two methodologies have been used: the traditional information criteria (AIC, HQ and SC) and an alternative criterion (IC(p,s)) which select simultaneously the lag order p and the rank structure s due to the WF restriction. A Monte-Carlo simulation is used in the analysis. The results indicate that the cost of ignoring additional WF restrictions in vector autoregressive modeling can be high, especially when SC criterion is used.

Box & Jenkins Model Identification:A Comparison of Methodologies

This paper focuses on a presentation of a comparison of a neuro-fuzzy back propagation network and Forecast automatic model Identification to identify automatically Box & Jenkins non seasonal models.Recently some combinations of neural networks and fuzzy logic technologies have being used to deal with uncertain and subjective problems. It is concluded on the basis of the obtained results that this type of approach is very powerful to be used

A Parsimonious Bootstrap Method to Model Natural Inflow Energy Series

The Brazilian energy generation and transmission system is quite peculiar in its dimension and characteristics. As such, it can be considered unique in the world. It is a high dimension hydrothermal system with huge participation of hydro plants. Such strong dependency on hydrological regimes implies uncertainties related to the energetic planning, requiring adequate modeling of the hydrological time series. This is carried out via stochastic simulations of monthly inflow series using the family of Periodic Autoregressive models, PAR(p), one for each period (month) of the year. In this paper it is shown the problems in fitting these models by the current system, particularly the identification of the autoregressive order “p” and the corresponding parameter estimation. It is followed by a proposal of a new approach to set both the model order and the parameters estimation of the PAR(p) models, using a nonparametric computational technique, known as Bootstrap. This technique allows the estimation of reliable confidence intervals for the model parameters. The obtained results using the Parsimonious Bootstrap Method of Moments (PBMOM) produced not only more parsimonious model orders but also adherent stochastic scenarios and, in the long range, lead to a better use of water resources in the energy operation planning

Garch model indentification using neural network

GARCH models are being largely used to estimate the volatility offinancial assets, and GARCH(1,1) is the one most used. However, identificationof GARCH models is not fully explored. Some specialist systems technology havebeen used in some applications of time series models such as time seriesclassification problems, ARMA models identification, as well as SARIMA. The aim of this paper is to develop an intelligent system that can accurately identifythe specification of GARCH models providing the right choice of the model to beused, thus avoiding the indiscriminate usage of GARCH(1,1) model

Long Memory Models to Generate Synthetic Hydrological Series

In Brazil, much of the energy production comes from hydroelectric plants whose planning is not trivial due to the strong dependence on rainfall regimes. This planning is accomplished through optimization models that use inputs such as synthetic hydrologic series generated from the statistical model PAR(p) (periodic autoregressive). Recently, Brazil began the search for alternative models able to capture the effects that the traditional model PAR(p) does not incorporate, such as long memory effects. Long memory in a time series can be defined as a significant dependence between lags separated by a long period of time. Thus, this research develops a study of the effects of long dependence in the series of streamflow natural energy in the South subsystem, in order to estimate a long memory model capable of generating synthetic hydrologic series

Predicción de series de tiempo con redes cascada-correlación

Las redes neuronales, y en particular los perceptrones multicapa (MLP), han sido reconocidos como una de las más poderosas técnicas para estimar series de tiempo; sin embargo, la técnica de redes cascada-correlación (CC) es un fuerte competidor para pronosticar series temporales pues incorpora mejoras a los problemas de identificabilidad estadística del modelo del MLP. En és- te artículo se compara el rendimiento de las redes CC respecto de otras técnicas, entre ellas el MLP, ANN y Arima, usando va- rias series de tiempo no lineales del mundo real, con el fin de determinar si las CC ofrecen buenos resultados en la práctica. Los resultados indican que las redes CC, en la mayoría de los casos, son superiores a los MLP, ANN y Arima, logrando errores me- nores en magnitud que los reportados en la literatura usando dichas técnicas, mientras que en relación a DAN2 se lograron e- rrores cercanos e incluso mejores.Artificial neural networks, especially multilayer perceptrons, have been recognised as being a powerful technique for forecasting nonlinear time series; however, cascade-correlation architecture is a strong competitor in this task due to it incorporating several advantages related to the statistical identification of multilayer perceptrons. This paper compares the accuracy of a cascade-co- rrelation neural network to the linear approach, multilayer perceptrons and dynamic architecture for artificial neural networks (DAN2) to determine whether the cascade-correlation network was able to forecast the time series being studied with more accu- racy. It was concluded that cascade-correlation was able to forecast time series with more accuracy than other approaches

Uso de Recibos de Ações nos Estados Unidos (ADRs) para Arbitragem

A eficiência de mercado é muito questionada por especialistas, alguns trabalhos sugerem oportunidades de arbitragem em diversas operações financeiras. Essas oportunidades podem ser explicadas principalmente pela assimetria de informação, pois a formação de preços no mercado acionário está diretamente ligada as informações, portanto o investidor que as possuir com mais rapidez, possui uma vantagem competitiva. O objetivo desse artigo é verificar a existência de oportunidades de arbitragem utilizando os Recibos de Ações nos Estados Unidos (ADRs –American Depositary Receipts), negociadas no mercado americano, e suas respectivas ações, negociadas no mercado nacional. Através do estudo de caso realizado com quatro empresas, desconsiderando os custos de transição, foram encontradas janelas de oportunidades para arbitragem. Dentre as empresas estudadas, duas apresentaram oportunidades freqüentes de arbitragem, sendo que uma delas a oportunidade de arbitragem pode ser modelada por modelo de série temporal

Electricity consumption forecasting using singular spectrum analysis

Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this paper, we also present the main results of a computational experiment with the monthly residential consumption of electricity in Brazil

GARCH MODEL INDENTIFICATION USING NEURAL NETWORK

GARCH models are being largely used to estimate the volatility offinancial assets, and GARCH(1,1) is the one most used. However, identificationof GARCH models is not fully explored. Some specialist systems technology havebeen used in some applications of time series models such as time seriesclassification problems, ARMA models identification, as well as SARIMA. The aim of this paper is to develop an intelligent system that can accurately identifythe specification of GARCH models providing the right choice of the model to beused, thus avoiding the indiscriminate usage of GARCH(1,1) model