Ivette Luna

Long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models

This paper proposes an operational policy for long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models using an open-loop feedback control framework. The optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available by a forecasting model based on a fuzzy inference system that captures the nonlinear correlation of consecutive inflows on an annual basis, then disaggregating it on a monthly basis. In order to focus on the ability of the approach to handle the stochastic nature of the problem, a case study with a single-reservoir system is considered. The performance of the proposed approach is evaluated by simulation over the historical inflow records and compared to that of the stochastic dynamic programming approach. The results show that the proposed approach leads to a better operational performance of the plant, providing lower spillages and higher average hydropower efficiency and generation.

Adaptive fuzzy system to forecast financial time series volatility

This paper introduces an adaptive fuzzy rule-based system applied as a financial time series model for volatility forecasting. The model is based on Takagi--Sugeno fuzzy systems and is built in two phases: In the first, the model uses the subtractive clustering algorithm to determine initial group structures in a reduced data set. In the second phase, the system is modified dynamically by adding and pruning operators and applying a recursive learning algorithm based on the expectation maximization optimization technique. The algorithm automatically determines the number of fuzzy rules necessary at each step, and one-step-ahead predictions are estimated and parameters updated. The model is applied to forecast financial time series volatility, considering daily values of the Sao Paulo stock exchange index, the Petrobras preferred stock prices, and the BRL/USD exchange rate. The model suggested is compared against generalized autoregressive conditional heteroskedasticity models. Experimental results show the adequacy of the adaptive fuzzy approach for volatility forecasting purposes.

Online estimation of stochastic volatility for asset returns

An important application of financial institutions is quantifying the risk involved in investing in an asset. These are various measures of risk like volatility or value-at-risk. To estimate them from data, a model for underlying financial time series has to be specified and parameters have to be estimated. In the following, we propose a framework for estimation of stochastic volatility of asset returns based on adaptive fuzzy rule based system. The model is based on Takagi-Sugeno fuzzy systems, and it is built in two phases. In the first phase, the model uses the Subtractive Clustering algorithm to determine group structures in a reduced data set for initialization purpose. In the second phase, the system is modified dynamically via adding and pruning operators and a recursive learning algorithm determines automatically the number of fuzzy rules necessary at each step, whereas one step ahead predictions are estimated and parameters are updated as well. The model is applied for forecasting financial time series volatility, considering daily values the REAL/USD exchange rate. The model suggested is compared against generalized autoregressive conditional heteroskedaticity models. Experimental results show the adequacy of the adaptative fuzzy approach for volatility forecasting purposes.

A Constructive-Fuzzy System Modeling for Time Series Forecasting

This paper suggests a constructive fuzzy system modeling for time series prediction. The model proposed is based on Takagi-Sugeno system and it comprises two phases. First, a fuzzy rule base structure is initialized and adjusted via the expectation maximization optimization technique (EM). In the second phase the initial system is modified and the structure is determined in a constructive fashion. This phase implements a constructive version of the EM algorithm, as well as adding and pruning operators. The constructive learning process reduces model complexity and defines automatically the structure of the system, providing an efficient time series model. The performance of the proposed model is verified for two series of the reduced data set at the Neural Forecasting Competition, for one to eighteen steps ahead forecasting. Results show the effectiveness of the constructive time series model.

Verifying the Use of Evolving Fuzzy Systems for Multi-Step Ahead Daily Inflow Forecasting

This study presents a prediction system based on evolving fuzzy models and a bottom-up approach for daily streamflow forecasting. Prediction models are based on adaptive Takagi-Sugeno fuzzy inference systems. These models make use of a sequential learning approach for updating their own structure and parameters over time according to changes or variations identified in the series, whereas rainfall and runoff information is processed at each time instant. Models are adjusted following a bottom-up approach, which consists of dividing the global problem into sub-problems, and each sub-problem is resolved separately. Final estimate is given by the aggregation of the parts. The proposed approach is compared to the Soil Moisture Accounting Procedure (SMAP), a hydrological model used by various hydroelectric companies of the Brazilian elec- trical sector. Simulation studies indicate that the evolving fuzzy system presents an adequate performance, leading to a promising alternative for daily streamflow forecasting. Indeed, results are improved when predictors are combined, primarily for a multi- step ahead prediction task.

Estimating the Brazilian Central Bank’s Reaction Function by Fuzzy Inference System

A modelling strategy based on the application of fuzzy inference system is shown to provide a powerful and efficient method for the identification of non-linear and linear economic relationships. The procedure is particularly suitable for the estimation of ill-defined systems in which there is considerable uncertainty about the nature and range of key input variables. In addition, no prior knowledge is required about the form of the underlying relationships. Trend, cyclical and irregular components of the model can all be processed in a single pass. The potential benefits of the fuzzy logic approach are illustrated using a model to explain regime changes in Brazilian nominal interest rates. The results suggest that the relationships in the model are basically non-linear.

An Adaptive Hybrid Model for Monthly Streamflow Forecasting

This paper suggests a new algorithm for generating Takagi-Sugeno fuzzy systems applied for time series prediction. The model proposed comprises two phases. First, the model structure is initialized in a constructive offline fashion, via an expectation maximization algorithm (EM). In the second phase the system is modified dynamically, via adding and pruning operators. At this stage, we propose a recursive learning algorithm, which is based on the EM optimization technique. This online algorithm determines automatically the number of rules necessary at each step. In this way, the model structure and parameters are updated during the adaptive training. The adaptive learning process reduces model complexity and defines automatically its structure providing an efficient model. The proposed approach is applied to build a time series model for monthly streamflow forecasting. The performance of the approach is compared with conventional models used to forecast streamflows. Results show similar errors, however, the suggested model presents a simpler and more parsimonious structure.

Streamflow forecasting using neural networks and fuzzy clustering techniques

Planning of hydroelectric systems is a complex and difficult task once it involves non-linear production characteristics and depends on numerous variables. A key variable is the streamflow. Streamflow values covering the entire planning period must be accurately forecasted because they strongly influence energy production. This paper suggests an application of a FIR neural network and a fuzzy clustering-based model to evaluate one-step and multi-step ahead predictions. Results are compared to the ones obtained by a periodic autoregressive model (PAR). It is interesting to apply a recurrent neural network for prediction task due to its ability for temporal processing and efficiency to solve nonlinear problems. The results show a generally better performance of the FIR neural network for the case studied.

Impacto dos contratos futuros do Ibovespa na volatilidade dos índices de ações no Brasil: uma análise na crise do subprime

Significant increasing in derivatives trading over the world markets has led to an interesting debate about futures contracts influences on spot prices. In this context, this paper aims to evaluate, during the subprime crisis, the influence of IBOVESPA futures price volatility on the spot price indices as follows: IBOVESPA, FGV-100, IBrX-50, IGC, SMLL and MLCX. We considered the period from August 2007 to April 2009, when the evidence of the crisis were intense until to be recovering of growth of stock market index. To assess causality-in-variance, tests proposed by Cheung and Ng (1996) and Hafner and Herwartz (2006) were employed, and the volatility was estimated by an univariate GARCH process. It was found that the volatility of IBOVESPA futures contract did not destabilize spot indices during the subprime crisis.

Fuzzy inference systems for synthetic monthly inflow time series generation

Inflow data plays an important role in water and energy resources planning and management. In general, due to the limited availability of historical inflow data, synthetic streamflow time series have been widely used for several applications such as mid- and long-term hydropower scheduling and the identification of hydrological processes. This paper explores the use of fuzzy inference systems for the identification of two hydrological processes, and its use in the generation of synthetic monthly inflow sequences. Experiments using Brazilian monthly records show that fuzzy systems provide a promising approach for synthetic streamflow time series generation.