Hybrid Volatility Forecasting Models Based on Machine Learning of High-Frequency Data

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ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

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Book Synopsis Hybrid Volatility Forecasting Models Based on Machine Learning of High-Frequency Data by : Xiaolin Wang

Download or read book Hybrid Volatility Forecasting Models Based on Machine Learning of High-Frequency Data written by Xiaolin Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility modeling and forecasting are crucial in risk management and pricing derivatives. High-frequency financial data are dynamic and affected by the microstructure noise. For the univariate case, we define the two-scale realized volatility estimator as the measure of the volatility of high-frequency financial data. Two main models for volatility, Generalized Autoregressive Conditional Heteroscedastic (GARCH) and Heterogeneous Autoregressive (HAR), are evaluated and compared for the realized volatility forecast of four major stock indices high-frequency data. We also consider the measures of jump component and heteroskedasticity of the error in the extended HAR models. For the improvement of forecasting accuracy of realized volatility, this dissertation develops hybrid forecasting models combining the GARCH and HAR family models with the machine learning methods, Support Vector Regression(SVR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) and Transformer. We construct hybrid models using the outputs of the GARCH and HAR family models. In the empirical application, we demonstrate improvements of the hybrid models for one-day ahead realized volatility forecast accuracy. The results show that the hybrid LSTM and Transformer based models provide more accurate forecasts than the other models. In the financial markets, it is well accepted that the volatilities are time-varying correlated across the indices. We construct two portfolios, the Index portfolio and the Forex portfolio. The Index portfolio contains three major stock indices, and the Forex portfolio includes three major exchange rates. We model the conditional covariances of the two portfolios with BEKK, DCC-GARCH, and Vector HAR. The hybrid models combine the estimations of traditional multivariate models and the machine learning framework. Results of the study indicate that for one-day ahead volatility matrix forecasting, these hybrid models can achieve better performance than the traditional models for the two portfolios.

Forecasting Volatility Using High Frequency Data

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ISBN 13 :
Total Pages : 37 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Forecasting Volatility Using High Frequency Data by : Peter Reinhard Hansen

Download or read book Forecasting Volatility Using High Frequency Data written by Peter Reinhard Hansen and published by . This book was released on 2018 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook chapter on volatility forecasting using high-frequency data, with surveys of reduced-form volatility forecasts and model-based volatility forecasts.

High Frequency Data, Frequency Domain Inference and Volatility Forecasting

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ISBN 13 :
Total Pages : 38 pages
Book Rating : 4.:/5 (318 download)

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Book Synopsis High Frequency Data, Frequency Domain Inference and Volatility Forecasting by : Jonathan H. Wright

Download or read book High Frequency Data, Frequency Domain Inference and Volatility Forecasting written by Jonathan H. Wright and published by . This book was released on 1999 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: While it is clear that the volatility of asset returns is serially correlated, there is no general agreement as to the most appropriate parametric model for characterizing this temporal dependence. In this paper, we propose a simple way of modeling financial market volatility using high frequency data. The method avoids using a tight parametric model, by instead simply fitting a long autoregression to log-squared, squared or absolute high frequency returns. This can either be estimated by the usual time domain method, or alternatively the autoregressive coefficients can be backed out from the smoothed periodogram estimate of the spectrum of log-squared, squared or absolute returns. We show how this approach can be used to construct volatility forecasts, which compare favorably with some leading alternatives in an out-of-sample forecasting exercise.

Exploiting High Frequency Data for Volatility Forecasting and Portfolio Selection

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ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (81 download)

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Book Synopsis Exploiting High Frequency Data for Volatility Forecasting and Portfolio Selection by : Yujia Hu

Download or read book Exploiting High Frequency Data for Volatility Forecasting and Portfolio Selection written by Yujia Hu and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: An instant may matter for the course of an entire life. It is with this idea that the present research had its inception. High frequency financial data are becoming increasingly available and this has triggered research in financial econometrics where information at high frequency can be exploited for different purposes. The most prominent example of this is the estimation and forecast of financial volatility. The research, chapter by chapter is summarized below. Chapter 1 provides empirical evidence on univariate realized volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. It examines leverage and volatility feedback effects among continuous and jump components of the S & P500 price and volatility dynamics, using recently developed methodologies to detect jumps and to disentangle their size from the continuous return and the continuous volatility. The research finds that jumps in return can improve forecasts of volatility, while jumps in volatility improve volatility forecasts to a lesser extent. Moreover, disentangling jump and continuous variations into signed semivariances further improves the out-of-sample performance of volatility forecasting models, with negative jump semivariance being highly more informative than positive jump semivariance. A simple autoregressive model is proposed and this is able to capture many empirical stylized facts while still remaining parsimonious in terms of number of parameters to be estimated. Chapter 2 investigates the out-of-sample performance and the economic value of multivariate forecasting models for volatility of exchange rate returns. It finds that, when the realized covariance matrix approximates the true latent covariance, a model that uses high frequency information for the correlation is more appropriate compared to alternative models that uses only low-frequency data. However multivariate FX returns standardized by the.

Forecasting Realized Volatility Using Machine Learning and Mixed-frequency Data (the Case of the Russian Stock Market)

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Publisher :
ISBN 13 : 9788073446154
Total Pages : pages
Book Rating : 4.4/5 (461 download)

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Book Synopsis Forecasting Realized Volatility Using Machine Learning and Mixed-frequency Data (the Case of the Russian Stock Market) by : Vladimir Pyrlik

Download or read book Forecasting Realized Volatility Using Machine Learning and Mixed-frequency Data (the Case of the Russian Stock Market) written by Vladimir Pyrlik and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Topics in Modeling Volatility Based on High-frequency Data

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ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (12 download)

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Book Synopsis Topics in Modeling Volatility Based on High-frequency Data by : Constantin Roth

Download or read book Topics in Modeling Volatility Based on High-frequency Data written by Constantin Roth and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first chapter, I compare the forecasting accuracy of different high-frequency based volatility models. The empirical analysis shows that the HEAVY and the Realized GARCH generally outperform the rest of the models. The inclusion of overnight returns considerably improves volatility forecasts for stocks across all models. Furthermore, the analysis shows that models based on realized volatility benefit much less from allowing leverage effects than do models based on daily returns. In the second chapter, the cause for this observation is investigated more deeply. I explain it by documenting that realized volatility tends to be higher on down-days than on up-days and that a similar asymmetry cannot be found in squared daily returns. I show that leverage effects are present already at high return-frequencies and that these are capable of generating asymmetries in realized variance but not in squared returns. In the third chapter, a conservative test based on the adaptive lasso is applied to investigate the optimal lag structure for modeling realized volatility dynamics. The empirical analysis shows that the optimal significant lag structure is time-varying and subject to drastic regime shifts. The accuracy of the HAR model can be explained by the observation that in many cases the relevant information for prediction is included in the first 22 lags. In the fourth chapter, a wild multiplicative bootstrap is introduced for M- and GMM estimators of time series. In Monte Carlo simulations, the wild bootstrap always outperforms inference which is based on standard asymptotic theory. Moreover, in most cases the accuracy of the wild bootstrap is also higher and more stable than that of the block bootstrap whose accuracy depends heavily on the choice of the block size.

Topics in Modeling Volatility Based on High-frequency Data

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (12 download)

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Book Synopsis Topics in Modeling Volatility Based on High-frequency Data by : Constantin A. Roth

Download or read book Topics in Modeling Volatility Based on High-frequency Data written by Constantin A. Roth and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the first chapter, I compare the forecasting accuracy of different high-frequency based volatility models. The empirical analysis shows that the HEAVY and the Realized GARCH generally outperform the rest of the models. The inclusion of overnight returns considerably improves volatility forecasts for stocks across all models. Furthermore, the analysis shows that models based on realized volatility benefit much less from allowing leverage effects than do models based on daily returns. In the second chapter, the cause for this observation is investigated more deeply. I explain it by documenting that realized volatility tends to be higher on down-days than on up-days and that a similar asymmetry cannot be found in squared daily returns. I show that leverage effects are present already at high return-frequencies and that these are capable of generating asymmetries in realized variance but not in squared returns. In the third chapter, a conservative test based on the adaptive lasso is applied to investigate the optimal lag structure for modeling realized volatility dynamics. The empirical analysis shows that the optimal significant lag structure is time-varying and subject to drastic regime shifts. The accuracy of the HAR model can be explained by the observation that in many cases the relevant information for prediction is included in the first 22 lags. In the fourth chapter, a wild multiplicative bootstrap is introduced for M- and GMM estimators of time series. In Monte Carlo simulations, the wild bootstrap always outperforms inference which is based on standard asymptotic theory. Moreover, in most cases the accuracy of the wild bootstrap is also higher and more stable than that of the block bootstrap whose accuracy depends heavily on the choice of the block size.

Biologically Inspired Techniques in Many-Criteria Decision Making

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Publisher : Springer Nature
ISBN 13 : 3030390330
Total Pages : 268 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Biologically Inspired Techniques in Many-Criteria Decision Making by : Satchidananda Dehuri

Download or read book Biologically Inspired Techniques in Many-Criteria Decision Making written by Satchidananda Dehuri and published by Springer Nature. This book was released on 2020-01-21 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems. This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing.

Forecasting High-Frequency Volatility Shocks

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Publisher : Springer
ISBN 13 : 3658125969
Total Pages : 188 pages
Book Rating : 4.6/5 (581 download)

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Book Synopsis Forecasting High-Frequency Volatility Shocks by : Holger Kömm

Download or read book Forecasting High-Frequency Volatility Shocks written by Holger Kömm and published by Springer. This book was released on 2016-02-08 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents a new strategy that unites qualitative and quantitative mass data in form of text news and tick-by-tick asset prices to forecast the risk of upcoming volatility shocks. Holger Kömm embeds the proposed strategy in a monitoring system, using first, a sequence of competing estimators to compute the unobservable volatility; second, a new two-state Markov switching mixture model for autoregressive and zero-inflated time-series to identify structural breaks in a latent data generation process and third, a selection of competing pattern recognition algorithms to classify the potential information embedded in unexpected, but public observable text data in shock and nonshock information. The monitor is trained, tested, and evaluated on a two year survey on the prime standard assets listed in the indices DAX, MDAX, SDAX and TecDAX.

Proceedings of the Twelfth International Conference on Management Science and Engineering Management

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Publisher : Springer
ISBN 13 : 3319933515
Total Pages : 1752 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Proceedings of the Twelfth International Conference on Management Science and Engineering Management by : Jiuping Xu

Download or read book Proceedings of the Twelfth International Conference on Management Science and Engineering Management written by Jiuping Xu and published by Springer. This book was released on 2018-06-25 with total page 1752 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings book is divided in 2 Volumes and 8 Parts. Part I is dedicated to Decision Support System, which is about the information system that supports business or organizational decision-making activities; Part II is on Computing Methodology, which is always used to provide the most effective algorithm for numerical solutions of various modeling problems; Part III presents Information Technology, which is the application of computers to store, study, retrieve, transmit and manipulate data, or information in the context of a business or other enterprise; Part IV is dedicated to Data Analysis, which is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making; Part V presents papers on Operational Management, which is about the plan, organization, implementation and control of the operation process; Part VI is on Project Management, which is about the initiating, planning, executing, controlling, and closing the work of a team to achieve specific goals and meet specific success criteria at the specified time in the field of engineering; Part VII presents Green Supply Chain, which is about the management of the flow of goods and services based on the concept of “low-carbon”; Part VIII is focused on Industry Strategy Management, which refers to the decision-making and management art of an industry or organization in a long-term and long-term development direction, objectives, tasks and policies, as well as resource allocation.

Volatility Trading with Machine Learning Forecasting Methods

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (115 download)

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Book Synopsis Volatility Trading with Machine Learning Forecasting Methods by : Sergio Andrés González Orjuela

Download or read book Volatility Trading with Machine Learning Forecasting Methods written by Sergio Andrés González Orjuela and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility trading has become a prominent alternative to the traditional stock trading as the rapid development of web-trading in recent years has reduced significantly the costs of operating in the market. Moreover, machine learning techniques have enabled traders to rely heavily on statistical decision-making models to enhance the commonly used technical analysis. In this paper, a machine learning approach is used to predict proxies of short-term implied volatility clusters with high-frequency data, in order to perform trading strategies using vanilla options on a commercial platform. The empirical results indicate that tree-based methods outperform linear models in classifying these clusters using the time of the day as a key variable in the forecasting task. Financial results were mixed due to the high costs of operating in a 5-hour horizon, but it was found that long positions on at the money straddle strategies expiring in one day were profitable. The framework developed here can be used by small investors as a guidance to implement and assess theoretical strategies in accessible markets.

Exploiting high frequency data for volatility forecasting and portfolio selection : [kumulative Dissertation]

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ISBN 13 :
Total Pages : 123 pages
Book Rating : 4.:/5 (81 download)

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Book Synopsis Exploiting high frequency data for volatility forecasting and portfolio selection : [kumulative Dissertation] by : Yujia Hu

Download or read book Exploiting high frequency data for volatility forecasting and portfolio selection : [kumulative Dissertation] written by Yujia Hu and published by . This book was released on 2012 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: An instant may matter for the course of an entire life. It is with this idea that the present research had its inception. High frequency financial data are becoming increasingly available and this has triggered research in financial econometrics where information at high frequency can be exploited for different purposes. The most prominent example of this is the estimation and forecast of financial volatility. The research, chapter by chapter is summarized below. Chapter 1 provides empirical evidence on univariate realized volatility forecasting in relation to asymmetries present in the dynamics of both return and volatility processes. It examines leverage and volatility feedback effects among continuous and jump components of the S&P500 price and volatility dynamics, using recently developed methodologies to detect jumps and to disentangle their size from the continuous return and the continuous volatility. The research finds that jumps in return can improve forecasts of volatility, while jumps in volatility improve volatility forecasts to a lesser extent. Moreover, disentangling jump and continuous variations into signed semivariances further improves the out-of-sample performance of volatility forecasting models, with negative jump semivariance being highly more informative than positive jump semivariance. A simple autoregressive model is proposed and this is able to capture many empirical stylized facts while still remaining parsimonious in terms of number of parameters to be estimated. Chapter 2 investigates the out-of-sample performance and the economic value of multivariate forecasting models for volatility of exchange rate returns. It finds that, when the realized covariance matrix approximates the true latent covariance, a model that uses high frequency information for the correlation is more appropriate compared to alternative models that uses only low-frequency data. However multivariate FX returns standardized by the.

Handbook of Volatility Models and Their Applications

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Publisher : John Wiley & Sons
ISBN 13 : 0470872519
Total Pages : 566 pages
Book Rating : 4.4/5 (78 download)

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Book Synopsis Handbook of Volatility Models and Their Applications by : Luc Bauwens

Download or read book Handbook of Volatility Models and Their Applications written by Luc Bauwens and published by John Wiley & Sons. This book was released on 2012-04-17 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.

Volatility Forecasting

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ISBN 13 :
Total Pages : 130 pages
Book Rating : 4.3/5 ( download)

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Book Synopsis Volatility Forecasting by : Torben Gustav Andersen

Download or read book Volatility Forecasting written by Torben Gustav Andersen and published by . This book was released on 2005 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volatility has been one of the most active and successful areas of research in time series econometrics and economic forecasting in recent decades. This chapter provides a selective survey of the most important theoretical developments and empirical insights to emerge from this burgeoning literature, with a distinct focus on forecasting applications. Volatility is inherently latent, and Section 1 begins with a brief intuitive account of various key volatility concepts. Section 2 then discusses a series of different economic situations in which volatility plays a crucial role, ranging from the use of volatility forecasts in portfolio allocation to density forecasting in risk management. Sections 3, 4 and 5 present a variety of alternative procedures for univariate volatility modeling and forecasting based on the GARCH, stochastic volatility and realized volatility paradigms, respectively. Section 6 extends the discussion to the multivariate problem of forecasting conditional covariances and correlations, and Section 7 discusses volatility forecast evaluation methods in both univariate and multivariate cases. Section 8 concludes briefly.

Volatility Forecasting Models

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ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (84 download)

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Book Synopsis Volatility Forecasting Models by : Marcial Messmer

Download or read book Volatility Forecasting Models written by Marcial Messmer and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We compare two more sophisticated GARCH-like models in terms of their out-of-sample forecasting power. Namely, we consider the tree-structured GARCH introduced by Audrino and Bühlmann (2001) and the GARCHMIDAS by Engle, Ghysels, and Sohn (2008). Additionally, we include the classical GARCH(1,1). We estimate the models using daily S&P 500 futures data. The models are evaluated out-of-sample based on the Diebold and Mariano Test, where the volatility proxy is constructed from high frequency data. We find evidence that the tree-structured GARCH is superior to the GARCH-MIDAS model and to the GARCH(1,1).

Price Forecasting in the Ontario Electricity Market Via TriConvGRU Hybrid Model

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ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis Price Forecasting in the Ontario Electricity Market Via TriConvGRU Hybrid Model by : Behdad Ehsani

Download or read book Price Forecasting in the Ontario Electricity Market Via TriConvGRU Hybrid Model written by Behdad Ehsani and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Electricity price forecasting is a challenging task for decision-makers in deregulated power markets due to the inherent characteristics of electricity prices, e.g., high frequency and volatility. Accordingly, we propose a novel hybrid Deep Learning model to forecast one-step, two-step, and three-step ahead electricity prices based on a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). Our model consists of three consecutive CNN-GRU models combined in parallel with different input data. We downsampled input data via pooling layers at the beginning of two streams of the model to capture different frequencies of price patterns concurrently. Also, a set of external variables, including previous prices, electricity load, generation, import and export, and weather data, were considered in our forecasting models to test whether these features improve the efficiency of the models. Finally, three experiments in various weeks of 2022 were carried out in the Ontario electricity market to assess the model. The results indicate that the proposed model reduced the forecasting error significantly by 63.3% in the first experiment, 41.8% in the second, and 28.2% in the third, on average. Also, the proposed model was compared with several baseline models, including statistical time-series, Machine Learning, and Deep Learning models and outperformed them. Furthermore, the comparison of results in univariate and multivariate settings indicated that adding variables to forecasting models did not help reduce forecasting errors.

Web and Big Data

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Publisher : Springer Nature
ISBN 13 : 9819723906
Total Pages : 536 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis Web and Big Data by : Xiangyu Song

Download or read book Web and Big Data written by Xiangyu Song and published by Springer Nature. This book was released on with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: