Noise Canceling in Volatility Forecasting using an Adaptive Neural Network Filter

Volatility forecasting models are becoming more accurate, but noise looks to be an inseparable part of these forecasts. Nonetheless, using adaptive filters to cancel the noise should help improve the performance of the forecasting models. Adaptive filters have the advantage of changing based on the environment. This feature is vital when they are used along with a model for volatility forecasting and error cancellation in the financial markets. Nonlinear Autoregressive (NAR) neural networks have simple structures, but they are efficient tools in error cancelation systems when working with non-stationary and random walk noise processes. For this research, an adaptive threshold filter is designed to respond to changes in its environment when a GARCH(1,1) model makes errors in its volatility forecast. It is shown that this filter can forecast the noise (errors) in the GARCH(1,1) outputs when there is a non-stationary time series of errors. The model reduces the mean squared errors by 42.9%. A sample portfolio of five stocks from the S&P 500 index from 4/2007 to 12/2010 is studied to illustrate the performance of the model

Applications of artificial neural networks in financial market forecasting

This thesis evaluates the utility of Artificial Neural Networks (ANNs) applied to financial market and macroeconomic forecasting. In application, ANNs are evaluated in comparison to traditional forecasting models to evaluate if their nonlinear and adaptive properties yield superior forecasting performance in terms of robustness and accuracy. Furthermore, as ANNs are data-driven models, an emphasis is placed on the data collection stage by compiling extensive candidate input variable pools, a task frequently underperformed by prior research. In evaluating their performance, ANNs are applied to the domains of: exchange rate forecasting, volatility forecasting, and macroeconomic forecasting. Regarding exchange rate forecasting, ANNs are applied to forecast the daily logarithmic returns of the EUR/USD over a short-term forecast horizon of one period. Initially, the analytic method of Technical Analysis (TA) and its sub-section of technical indicators are utilized to compile an extensive candidate input variable pool featuring standard and advanced technical indicators measuring all technical aspects of the EUR/USD time series. The candidate input variable pool is then subjected to a two-stage Input Variable Selection (IVS) process, producing an informative subset of technical indicators to serve as inputs to the ANNs. A collection of ANNs is then trained and tested on the EUR/USD time series data with their performance evaluated over a 5-year sample period (2012 to 2016), reserving the last two years for out of sample testing. A Moving Average Convergence Divergence (MACD) model serves as a benchmark with the in-sample and out-of-sample empirical results demonstrating the MACD is a superior forecasting model across most forecast evaluation metrics. For volatility forecasting, ANNs are applied to forecast the volatility of the Nikkei 225 Index over a short-term forecast horizon of one period. Initially, an extensive candidate input variable pool is compiled consisting of implied volatility models and historical volatility models. The candidate input variable pool is then subjected to a two-stage IVS process. A collection of ANNs is then trained and tested on the Nikkei 225 Index time series data with their performance evaluated over a 4-year sample period (2014 to 2017), reserving the last year for out-of-sample testing. A GARCH (1,1) model serves as a benchmark with the out-of-sample empirical results finding the GARCH (1,1) model to be the superior volatility forecasting model. The research concludes with ANNs applied to macroeconomic forecasting, where ANNs are applied to forecast the monthly per cent-change in U.S. civilian unemployment and the quarterly per cent-change in U.S. Gross Domestic Product (GDP). For both studies, an extensive candidate input variable pool is compiled using relevant macroeconomic indicator data sourced from the Federal Bank of St Louis. The candidate input variable pools are then subjected to a two-stage IVS process. A collection of ANNs is trained and tested on the U.S. unemployment time series data (UNEMPLOY) and U.S. GDP time series data. The sample periods are (1972 to 2017) and (1960 to 2016) respectively, reserving the last 20% of data for out of sample testing. In both studies, the performance of the ANNs is benchmarked against a Support Vector Regression (SVR) model and a Naïve forecast. In both studies, the ANNs outperform the SVR benchmark model. The empirical results demonstrate that ANNs are superior forecasting models in the domain of macroeconomic forecasting, with the Modular Neural Network performing notably well. However, the empirical results question the utility of ANNs in the domains of exchange rate forecasting and volatility forecasting. A MACD model outperforms ANNs in exchange rate forecasting both in-sample and out-of-sample, and a GARCH (1,1) model outperforms ANNs in volatility forecasting

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems

Review of Forecasting Univariate Time-series Data with Application to Water-Energy Nexus Studies & Proposal of Parallel Hybrid SARIMA-ANN Model

The necessary materials for most human activities are water and energy. Integrated analysis to accurately forecast water and energy consumption enables the implementation of efficient short and long-term resource management planning as well as expanding policy and research possibilities for the supportive infrastructure. However, the integral relationship between water and energy (water-energy nexus) poses a difficult problem for modeling. The accessibility and physical overlay of data sets related to water-energy nexus is another main issue for a reliable water-energy consumption forecast. The framework of urban metabolism (UM) uses several types of data to build a global view and highlight issues of inefficiency within the network. Failure to view the whole system contributes to the inability to comprehend the complexity and interconnectivity of the issues within the system. This complexity is found in most systems, especially with systems that must be able to support and react to vacillating human interaction and behavior. One approach to address the limitations of data accessibility and model inflexibility is through the application of univariate time-series with heterogeneous hybrid modeling addresses. Time-series forecasting uses past observations of the same variable(s) to analyze and separate the pattern from white noise to define underlying relationships to predict future behavior. There are various linear and non-linear models utilized to forecast time-series data sets; however, ground truth data sets with extreme seasonal variation are neither pure linear nor pure non-linear. This truth has propelled model building into hybrid model frameworks to combine linear and non-linear methodologies to reduce the fallacies of both model frameworks with the other\u27s strengths. This problem report works to illustrate the limitations of complex WEN studies, build a timeline of hybrid modeling analysis using univariate time-series data, and develop a parallel hybrid SARIMA-ANN model framework to increase univariate time-series analysis capabilities in order to address previously discussed WEN study limitations. The parallel Hybrid SARIMA – ANN model performs better in comparison to SARIMA, ANN, and Series hybrid SARIMA-ANN; and shows promise for research expansion with structure flexibility to expand with additional variables

Disaggregation: Inferring Daily Gas Flow from Billing Cycle Data

Local natural gas distribution companies rely on accurate forecasts of daily demand to buy gas and deliver it to their customers. To forecast consumption, mathematical models with inputs such as weather and historical daily demand are considered. Many needs exist in the energy industry where the frequency of measurement is different from demanded. When the needed forecast frequency is higher than the measurements, disaggregation approaches are needed. We built multi-parameter linear regression models using monthly data. Several decoration methods in the disaggregation process are developed to improve the model accuracy. Prior-day weather adjustment is used to capture the daily fluctuation of gas consumption as a result of the temperature differences between current day and prior day. Also, behavioral patterns in gas consumption are incorporated in the models to account for consumption patterns in weekdays vs. weekend and days of week. Furthermore, we consider long-term characteristics in the gas consumption data originated from population changes, differences in building efficiency, and economic impacts. Firstly, Base Load Trend and later Heat Load Trend are considered in the linear regression models. Secondly, historical flow is detrended to act like the most recent data by altering its characteristics to approximate a stationary customer base with current behavioral patterns. Root Mean Square Error, Mean Absolute Percent Error, and Weighted Mean Absolute Percent Error are used as means for assessing the performance of our approaches. All decorations enhance forecasts, with Prior-Day adjustment as the most effective. The combination of decorations leads to further enhancements. Inclusion of detrending models decreases the forecast errors significantly. For geographic areas that have experienced identifiable trends, considering Base Heat Load Trend in the model shows the most improvement in detrending models. Extensive comparisons between decoration and detrending algorithms and the combination of these models show all methods enhance daily gas demand forecast accuracies. The combination of Base Heat Load Trend model, Day of the Week, and Prior-Day adjustment is most effective to improve the accuracy of daily demand forecasts from historical monthly gas consumption without need to any additional infrastructure to save Local Distribution Companies and customers a large amount of money

Runtime data center temperature prediction using Grammatical Evolution techniques

Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEMinisterio de Economía y Competitividad (MINECO)pu

Nonlinear Dynamics

This volume covers a diverse collection of topics dealing with some of the fundamental concepts and applications embodied in the study of nonlinear dynamics. Each of the 15 chapters contained in this compendium generally fit into one of five topical areas: physics applications, nonlinear oscillators, electrical and mechanical systems, biological and behavioral applications or random processes. The authors of these chapters have contributed a stimulating cross section of new results, which provide a fertile spectrum of ideas that will inspire both seasoned researches and students