{"product_id":"wavelet-neural-networks-isbn-9781118592526","title":"Wavelet Neural Networks","description":"\u003cp\u003e\u003cb\u003eA step-by-step introduction to modeling, training, and forecasting using wavelet networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eWavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification\u003c\/i\u003e presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification.\u003c\/p\u003e \u003cp\u003eThe authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, \u003ci\u003eWavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification\u003c\/i\u003e also includes:\u003c\/p\u003e \u003cp\u003e• Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence\u003c\/p\u003e \u003cp\u003e• Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction\u003c\/p\u003e \u003cp\u003e• An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks\u003c\/p\u003e \u003cp\u003e• Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals\u003c\/p\u003e \u003cp\u003eIdeal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics.\u003c\/p\u003e  \u003cp\u003e\u003ci\u003ePreface xiii\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Machine Learning and Financial Engineering 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFinancial Engineering 2\u003c\/p\u003e \u003cp\u003eFinancial Engineering and Related Research Areas 3\u003c\/p\u003e \u003cp\u003eFunctions of Financial Engineering 5\u003c\/p\u003e \u003cp\u003eApplications of Machine Learning in Finance 6\u003c\/p\u003e \u003cp\u003eFrom Neural to Wavelet Networks 8\u003c\/p\u003e \u003cp\u003eWavelet Analysis 8\u003c\/p\u003e \u003cp\u003eExtending the Fourier Transform: The Wavelet Analysis Paradigm 10\u003c\/p\u003e \u003cp\u003eNeural Networks 17\u003c\/p\u003e \u003cp\u003eWavelet Neural Networks 19\u003c\/p\u003e \u003cp\u003eApplications of Wavelet Neural Networks in Financial Engineering, Chaos, and Classification 21\u003c\/p\u003e \u003cp\u003eBuilding Wavelet Networks 23\u003c\/p\u003e \u003cp\u003eVariable Selection 23\u003c\/p\u003e \u003cp\u003eModel Selection 24\u003c\/p\u003e \u003cp\u003eModel Adequacy Testing 25\u003c\/p\u003e \u003cp\u003eBook Outline 25\u003c\/p\u003e \u003cp\u003eReferences 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Neural Networks 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eParallel Processing 36\u003c\/p\u003e \u003cp\u003eProcessing Units 37\u003c\/p\u003e \u003cp\u003eActivation Status and Activation Rules 37\u003c\/p\u003e \u003cp\u003eConnectivity Model 39\u003c\/p\u003e \u003cp\u003ePerceptron 41\u003c\/p\u003e \u003cp\u003eThe Approximation Theorem 42\u003c\/p\u003e \u003cp\u003eThe Delta Rule 42\u003c\/p\u003e \u003cp\u003eBackpropagation Neural Networks 44\u003c\/p\u003e \u003cp\u003eMultilayer Feedforward Networks 44\u003c\/p\u003e \u003cp\u003eThe Generalized Delta Rule 45\u003c\/p\u003e \u003cp\u003eBackpropagation in Practice 49\u003c\/p\u003e \u003cp\u003eTraining with Backpropagation 51\u003c\/p\u003e \u003cp\u003eNetwork Paralysis 54\u003c\/p\u003e \u003cp\u003eLocal Minima 54\u003c\/p\u003e \u003cp\u003eNonunique Solutions 56\u003c\/p\u003e \u003cp\u003eConfiguration Reference 56\u003c\/p\u003e \u003cp\u003eConclusions 59\u003c\/p\u003e \u003cp\u003eReferences 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Wavelet Neural Networks 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWavelet Neural Networks for Multivariate Process Modeling 62\u003c\/p\u003e \u003cp\u003eStructure of a Wavelet Neural Network 62\u003c\/p\u003e \u003cp\u003eInitialization of the Parameters of the Wavelet Network 64\u003c\/p\u003e \u003cp\u003eTraining a Wavelet Network with Backpropagation 69\u003c\/p\u003e \u003cp\u003eStopping Conditions for Training 72\u003c\/p\u003e \u003cp\u003eEvaluating the Initialization Methods 73\u003c\/p\u003e \u003cp\u003eConclusions 77\u003c\/p\u003e \u003cp\u003eReferences 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Model Selection: Selecting the Architecture of the Network 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Usual Practice 82\u003c\/p\u003e \u003cp\u003eEarly Stopping 82\u003c\/p\u003e \u003cp\u003eRegularization 83\u003c\/p\u003e \u003cp\u003ePruning 84\u003c\/p\u003e \u003cp\u003eMinimum Prediction Risk 86\u003c\/p\u003e \u003cp\u003eEstimating the Prediction Risk Using Information Criteria 87\u003c\/p\u003e \u003cp\u003eEstimating the Prediction Risk Using Sampling Techniques 89\u003c\/p\u003e \u003cp\u003eBootstrapping 91\u003c\/p\u003e \u003cp\u003eCross-Validation 94\u003c\/p\u003e \u003cp\u003eModel Selection Without Training 95\u003c\/p\u003e \u003cp\u003eEvaluating the Model Selection Algorithm 97\u003c\/p\u003e \u003cp\u003eCase 1: Sinusoid and Noise with Decreasing Variance 98\u003c\/p\u003e \u003cp\u003eCase 2: Sum of Sinusoids and Cauchy Noise 100\u003c\/p\u003e \u003cp\u003eAdaptive Networks and Online Synthesis 103\u003c\/p\u003e \u003cp\u003eConclusions 104\u003c\/p\u003e \u003cp\u003eReferences 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Variable Selection: Determining the Explanatory Variables 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExisting Algorithms 108\u003c\/p\u003e \u003cp\u003eSensitivity Criteria 110\u003c\/p\u003e \u003cp\u003eModel Fitness Criteria 112\u003c\/p\u003e \u003cp\u003eAlgorithm for Selecting the Significant Variables 114\u003c\/p\u003e \u003cp\u003eResampling Methods for the Estimation of Empirical Distributions 116\u003c\/p\u003e \u003cp\u003eEvaluating the Variable Significance Criteria 117\u003c\/p\u003e \u003cp\u003eCase 1: Sinusoid and Noise with Decreasing Variance 117\u003c\/p\u003e \u003cp\u003eCase 2: Sum of Sinusoids and Cauchy Noise 120\u003c\/p\u003e \u003cp\u003eConclusions 123\u003c\/p\u003e \u003cp\u003eReferences 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Model Adequacy: Determining a Network’s Future Performance 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTesting the residuals 126\u003c\/p\u003e \u003cp\u003eTesting for Serial Correlation in the Residuals 127\u003c\/p\u003e \u003cp\u003eEvaluation Criteria for the Prediction Ability of the Wavelet Network 129\u003c\/p\u003e \u003cp\u003eMeasuring the Accuracy of the Predictions 129\u003c\/p\u003e \u003cp\u003eScatter Plots 131\u003c\/p\u003e \u003cp\u003eLinear Regression Between Forecasts and Targets 132\u003c\/p\u003e \u003cp\u003eMeasuring the Ability to Predict the Change in Direction 136\u003c\/p\u003e \u003cp\u003eTwo Simulated Cases 137\u003c\/p\u003e \u003cp\u003eCase 1: Sinusoid and Noise with Decreasing Variance 137\u003c\/p\u003e \u003cp\u003eCase 2: Sum of Sinusoids and Cauchy Noise 142\u003c\/p\u003e \u003cp\u003eClassification 146\u003c\/p\u003e \u003cp\u003eAssumptions and Objectives of Discriminant Analysis 146\u003c\/p\u003e \u003cp\u003eValidation of the Discriminant Function 148\u003c\/p\u003e \u003cp\u003eEvaluating the Classification Ability of a Wavelet Network 150\u003c\/p\u003e \u003cp\u003eCase 3: Classification Example on Bankruptcy 153\u003c\/p\u003e \u003cp\u003eConclusions 156\u003c\/p\u003e \u003cp\u003eReferences 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Modeling Uncertainty: From Point Estimates to Prediction Intervals 159\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Usual Practice 160\u003c\/p\u003e \u003cp\u003eConfidence and Prediction Intervals 161\u003c\/p\u003e \u003cp\u003eConstructing Confidence Intervals 164\u003c\/p\u003e \u003cp\u003eThe Bagging Method 164\u003c\/p\u003e \u003cp\u003eThe Balancing Method 165\u003c\/p\u003e \u003cp\u003eConstructing Prediction Intervals 166\u003c\/p\u003e \u003cp\u003eThe Bagging Method 167\u003c\/p\u003e \u003cp\u003eThe Balancing Method 168\u003c\/p\u003e \u003cp\u003eEvaluating the Methods for Constructing Confidence and Prediction Intervals 168\u003c\/p\u003e \u003cp\u003eConclusions 170\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Modeling Financial Temperature Derivatives 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWeather Derivatives 174\u003c\/p\u003e \u003cp\u003ePricing and Modeling Methods 175\u003c\/p\u003e \u003cp\u003eData Description and Preprocessing 176\u003c\/p\u003e \u003cp\u003eData Examination 176\u003c\/p\u003e \u003cp\u003eModel for the Daily Average Temperature: Gaussian Ornstein–Uhlenbeck Process with Lags and Time-Varying Mean Reversion 179\u003c\/p\u003e \u003cp\u003eEstimation Using Wavelet Networks 183\u003c\/p\u003e \u003cp\u003eVariable Selection 183\u003c\/p\u003e \u003cp\u003eModel Selection 187\u003c\/p\u003e \u003cp\u003eInitialization and Training 187\u003c\/p\u003e \u003cp\u003eConfidence and Prediction Intervals 189\u003c\/p\u003e \u003cp\u003eOut-of-Sample Comparison 189\u003c\/p\u003e \u003cp\u003eConclusions 191\u003c\/p\u003e \u003cp\u003eReferences 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Modeling Financial Wind Derivatives 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModeling the Daily Average Wind Speed 199\u003c\/p\u003e \u003cp\u003eLinear ARMA Model 202\u003c\/p\u003e \u003cp\u003eWavelet Networks for Wind Speed Modeling 206\u003c\/p\u003e \u003cp\u003eVariable Selection 206\u003c\/p\u003e \u003cp\u003eModel Selection 209\u003c\/p\u003e \u003cp\u003eInitialization and Training 209\u003c\/p\u003e \u003cp\u003eModel Adequacy 209\u003c\/p\u003e \u003cp\u003eSpeed of Mean Reversion and Seasonal Variance 211\u003c\/p\u003e \u003cp\u003eForecasting Daily Average Wind Speeds 212\u003c\/p\u003e \u003cp\u003eConclusions 215\u003c\/p\u003e \u003cp\u003eReferences 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Predicting Chaotic Time Series 219\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMackey–Glass Equation 220\u003c\/p\u003e \u003cp\u003eModel Selection 221\u003c\/p\u003e \u003cp\u003eInitialization and Training 221\u003c\/p\u003e \u003cp\u003eModel Adequacy 222\u003c\/p\u003e \u003cp\u003ePredicting the Evolution of the Chaotic Mackey–Glass Time Series 225\u003c\/p\u003e \u003cp\u003eConfidence and Prediction Intervals 226\u003c\/p\u003e \u003cp\u003eConclusions 228\u003c\/p\u003e \u003cp\u003eReferences 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Classification of Breast Cancer Cases 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart A: Classification of Breast Cancer 232\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eModel Selection 232\u003c\/p\u003e \u003cp\u003eInitialization and Training 233\u003c\/p\u003e \u003cp\u003eClassification 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart B: Cross-Validation in Breast Cancer Classification in Wisconsin 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eVariable Selection 235\u003c\/p\u003e \u003cp\u003eModel Selection 237\u003c\/p\u003e \u003cp\u003eInitialization and Training 238\u003c\/p\u003e \u003cp\u003eClassification Power of the Full and Reduced Models 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart C: Classification of Breast Cancer (Continued) 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eClassification 241\u003c\/p\u003e \u003cp\u003e\u003ci\u003eConclusions 243\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eReferences 244\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eIndex 245\u003c\/i\u003e\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eAntonios K. Alexandridis, PhD,\u003c\/b\u003e is Lecturer of Finance in the School of Mathematics, Statistics, and Actuarial Science at the University of Kent. Dr. Alexandridis’ research interests include financial derivative modeling, pricing and forecasting, machine learning, and neural and wavelet networks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAchilleas D. Zapranis, PhD,\u003c\/b\u003e is Associate Professor in the Department of Finance and Accounting at the University of Macedonia, where he is also Vice Rector of Economic Planning and Development. In addition, Dr. Zapranis is a member of the Board of Directors of Thessaloniki’s Innovation Zone.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eA step-by-step introduction to modeling, training, and forecasting using wavelet networks\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eWavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification\u003c\/i\u003e presents the statistical model identification framework that is needed to successfully apply wavelet networks as well as extensive comparisons of alternate methods. Providing a concise and rigorous treatment for constructing optimal wavelet networks, the book links mathematical aspects of wavelet network construction to statistical modeling and forecasting applications in areas such as finance, chaos, and classification.\u003c\/p\u003e \u003cp\u003eThe authors ensure that readers obtain a complete understanding of model identification by providing in-depth coverage of both model selection and variable significance testing. Featuring an accessible approach with introductory coverage of the basic principles of wavelet analysis, \u003ci\u003eWavelet Neural\u003c\/i\u003e \u003ci\u003eNetworks: With Applications in Financial Engineering, Chaos, and Classification\u003c\/i\u003e also includes:\u003c\/p\u003e \u003cp\u003e• Methods that can be easily implemented or adapted by researchers, academics, and professionals in identification and modeling for complex nonlinear systems and artificial intelligence\u003c\/p\u003e \u003cp\u003e• Multiple examples and thoroughly explained procedures with numerous applications ranging from financial modeling and financial engineering, time series prediction and construction of confidence and prediction intervals, and classification and chaotic time series prediction\u003c\/p\u003e \u003cp\u003e• An extensive introduction to neural networks that begins with regression models and builds to more complex frameworks\u003c\/p\u003e \u003cp\u003e• Coverage of both the variable selection algorithm and the model selection algorithm for wavelet networks in addition to methods for constructing confidence and prediction intervals\u003c\/p\u003e \u003cp\u003eIdeal as a textbook for MBA and graduate-level courses in applied neural network modeling, artificial intelligence, advanced data analysis, time series, and forecasting in financial engineering, the book is also useful as a supplement for courses in informatics, identification and modeling for complex nonlinear systems, and computational finance. In addition, the book serves as a valuable reference for researchers and practitioners in the fields of mathematical modeling, engineering, artificial intelligence, decision science, neural networks, and finance and economics.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990469394661,"sku":"NP9781118592526","price":115.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118592526.jpg?v=1761787952","url":"https:\/\/k12savings.com\/products\/wavelet-neural-networks-isbn-9781118592526","provider":"K12savings","version":"1.0","type":"link"}