{"product_id":"multivariate-time-series-analysis-and-applications-hardback-9781119502852","title":"Multivariate Time Series Analysis and Applications (Hardback) 9781119502852","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMultivariate Time Series Analysis and Applications\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eWilliam W. S. Wei (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119502852, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 15 March 2019\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e536 pages\u003cbr\u003e24.6 x 17.2 x 3 cm, 0.93 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eAn essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFollowing the highly successful and much lauded book, \u003ci\u003eTime Series Analysis—Univariate and Multivariate Methods\u003c\/i\u003e, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series.\u003c\/p\u003e \u003cp\u003eWith the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. \u003ci\u003eMultivariate Time Series Analysis and its Applications\u003c\/i\u003e provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eWritten by bestselling author and leading expert in the field\u003c\/li\u003e \u003cli\u003eCovers topics not yet explored in current multivariate books\u003c\/li\u003e \u003cli\u003eFeatures classroom tested material\u003c\/li\u003e \u003cli\u003eWritten specifically for time series courses\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eMultivariate Time Series Analysis and its Applications\u003c\/i\u003e is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering. \u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eAbout the author xiii\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAbout the Companion website xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Fundamental Concepts and Issues in Multivariate Time Series Analysis 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Fundamental concepts 3\u003c\/p\u003e \u003cp\u003e1.2.1 Correlation and partial correlation matrix functions 3\u003c\/p\u003e \u003cp\u003e1.2.2 Vector white noise process 7\u003c\/p\u003e \u003cp\u003e1.2.3 Moving average and autoregressive representations of vector processes 7\u003c\/p\u003e \u003cp\u003eProjects 9\u003c\/p\u003e \u003cp\u003eReferences 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Vector Time Series Models 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Vector moving average processes 11\u003c\/p\u003e \u003cp\u003e2.2 Vector autoregressive processes 14\u003c\/p\u003e \u003cp\u003e2.2.1 Granger causality 18\u003c\/p\u003e \u003cp\u003e2.3 Vector autoregressive moving average processes 18\u003c\/p\u003e \u003cp\u003e2.4 Nonstationary vector autoregressive moving average processes 21\u003c\/p\u003e \u003cp\u003e2.5 Vector time series model building 21\u003c\/p\u003e \u003cp\u003e2.5.1 Identification of vector time series models 21\u003c\/p\u003e \u003cp\u003e2.5.2 Sample moments of a vector time series 22\u003c\/p\u003e \u003cp\u003e2.5.2.1 Sample mean and sample covariance matrices 22\u003c\/p\u003e \u003cp\u003e2.5.2.2 Sample correlation matrix function 23\u003c\/p\u003e \u003cp\u003e2.5.2.3 Sample partial correlation matrix function and extended cross-correlation matrices 24\u003c\/p\u003e \u003cp\u003e2.5.3 Parameter estimation, diagnostic checking, and forecasting 24\u003c\/p\u003e \u003cp\u003e2.5.4 Cointegration in vector time series 25\u003c\/p\u003e \u003cp\u003e2.6 Seasonal vector time series model 26\u003c\/p\u003e \u003cp\u003e2.7 Multivariate time series outliers 27\u003c\/p\u003e \u003cp\u003e2.7.1 Types of multivariate time series outliers and detections 27\u003c\/p\u003e \u003cp\u003e2.7.2 Outlier detection through projection pursuit 29\u003c\/p\u003e \u003cp\u003e2.8 Empirical examples 32\u003c\/p\u003e \u003cp\u003e2.8.1 First model of US monthly retail sales revenue 32\u003c\/p\u003e \u003cp\u003e2.8.2 Second model of US monthly retail sales revenue 43\u003c\/p\u003e \u003cp\u003e2.8.3 US macroeconomic indicators 47\u003c\/p\u003e \u003cp\u003e2.8.4 Unemployment rates with outliers 58\u003c\/p\u003e \u003cp\u003eSoftware code 65\u003c\/p\u003e \u003cp\u003eProjects 100\u003c\/p\u003e \u003cp\u003eReferences 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multivariate Time Series Regression Models 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 105\u003c\/p\u003e \u003cp\u003e3.2 Multivariate multiple time series regression models 105\u003c\/p\u003e \u003cp\u003e3.2.1 The classical multiple regression model 105\u003c\/p\u003e \u003cp\u003e3.2.2 Multivariate multiple regression model 106\u003c\/p\u003e \u003cp\u003e3.3 Estimation of the multivariate multiple time series regression model 108\u003c\/p\u003e \u003cp\u003e3.3.1 The Generalized Least Squares (GLS) estimation 108\u003c\/p\u003e \u003cp\u003e3.3.2 Empirical Example I – U.S. retail sales and some national indicators 109\u003c\/p\u003e \u003cp\u003e3.4 Vector time series regression models 114\u003c\/p\u003e \u003cp\u003e3.4.1 Extension of a VAR model to VARX models 114\u003c\/p\u003e \u003cp\u003e3.4.2 Empirical Example II – VARX models for U.S. retail sales and some national indicators 115\u003c\/p\u003e \u003cp\u003e3.5 Empirical Example III – Total mortality and air pollution in California 120\u003c\/p\u003e \u003cp\u003eSoftware code 129\u003c\/p\u003e \u003cp\u003eProjects 137\u003c\/p\u003e \u003cp\u003eReferences 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Principle Component Analysis of Multivariate Time Series 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 139\u003c\/p\u003e \u003cp\u003e4.2 Population PCA 140\u003c\/p\u003e \u003cp\u003e4.3 Implications of PCA 141\u003c\/p\u003e \u003cp\u003e4.4 Sample principle components 142\u003c\/p\u003e \u003cp\u003e4.5 Empirical examples 145\u003c\/p\u003e \u003cp\u003e4.5.1 Daily stock returns from the first set of 10 stocks 145\u003c\/p\u003e \u003cp\u003e4.5.1.1 The PCA based on the sample covariance matrix 147\u003c\/p\u003e \u003cp\u003e4.5.1.2 The PCA based on the sample correlation matrix 150\u003c\/p\u003e \u003cp\u003e4.5.2 Monthly Consumer Price Index (CPI) from five sectors 152\u003c\/p\u003e \u003cp\u003e4.5.2.1 The PCA based on the sample covariance matrix 153\u003c\/p\u003e \u003cp\u003e4.5.2.2 The PCA based on the sample correlation matrix 154\u003c\/p\u003e \u003cp\u003eSoftware code 157\u003c\/p\u003e \u003cp\u003eProjects 160\u003c\/p\u003e \u003cp\u003eReferences 161\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Factor Analysis of Multivariate Time Series 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 163\u003c\/p\u003e \u003cp\u003e5.2 The orthogonal factor model 163\u003c\/p\u003e \u003cp\u003e5.3 Estimation of the factor model 165\u003c\/p\u003e \u003cp\u003e5.3.1 The principal component method 165\u003c\/p\u003e \u003cp\u003e5.3.2 Empirical Example I – Model 1 on daily stock returns from the second set of 10 stocks 166\u003c\/p\u003e \u003cp\u003e5.3.3 The maximum likelihood method 169\u003c\/p\u003e \u003cp\u003e5.3.4 Empirical Example II – Model 2 on daily stock returns from the second set of 10 stocks 173\u003c\/p\u003e \u003cp\u003e5.4 Factor rotation 175\u003c\/p\u003e \u003cp\u003e5.4.1 Orthogonal rotation 176\u003c\/p\u003e \u003cp\u003e5.4.2 Oblique rotation 176\u003c\/p\u003e \u003cp\u003e5.4.3 Empirical Example III – Model 3 on daily stock returns from the second set of 10 stocks 177\u003c\/p\u003e \u003cp\u003e5.5 Factor scores 178\u003c\/p\u003e \u003cp\u003e5.5.1 Introduction 178\u003c\/p\u003e \u003cp\u003e5.5.2 Empirical Example IV – Model 4 on daily stock returns from the second set of 10 stocks 179\u003c\/p\u003e \u003cp\u003e5.6 Factor models with observable factors 181\u003c\/p\u003e \u003cp\u003e5.7 Another empirical example – Yearly U.S. sexually transmitted diseases (STD) 183\u003c\/p\u003e \u003cp\u003e5.7.1 Principal components analysis (PCA) 183\u003c\/p\u003e \u003cp\u003e5.7.1.1 PCA for standardized Z\u003csub\u003et\u003c\/sub\u003e 183\u003c\/p\u003e \u003cp\u003e5.7.1.2 PCA for unstandardized Z\u003csub\u003et\u003c\/sub\u003e 186\u003c\/p\u003e \u003cp\u003e5.7.2 Factor analysis 186\u003c\/p\u003e \u003cp\u003e5.8 Concluding remarks 193\u003c\/p\u003e \u003cp\u003eSoftware code 194\u003c\/p\u003e \u003cp\u003eProjects 200\u003c\/p\u003e \u003cp\u003eReferences 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Multivariate GARCH Models 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 203\u003c\/p\u003e \u003cp\u003e6.2 Representations of multivariate GARCH models 204\u003c\/p\u003e \u003cp\u003e6.2.1 VEC and DVEC models 204\u003c\/p\u003e \u003cp\u003e6.2.2 Constant Conditional Correlation (CCC) models 206\u003c\/p\u003e \u003cp\u003e6.2.3 BEKK models 207\u003c\/p\u003e \u003cp\u003e6.2.4 Factor models 208\u003c\/p\u003e \u003cp\u003e6.3 O-GARCH and GO-GARCH models 209\u003c\/p\u003e \u003cp\u003e6.4 Estimation of GO-GARCH models 210\u003c\/p\u003e \u003cp\u003e6.4.1 The two-step estimation method 210\u003c\/p\u003e \u003cp\u003e6.4.2 The weighted scatter estimation method 211\u003c\/p\u003e \u003cp\u003e6.5 Properties of the weighted scatter estimator 213\u003c\/p\u003e \u003cp\u003e6.5.1 Asymptotic distribution and statistical inference 213\u003c\/p\u003e \u003cp\u003e6.5.2 Combining information from different weighting functions 214\u003c\/p\u003e \u003cp\u003e6.6 Empirical examples 215\u003c\/p\u003e \u003cp\u003e6.6.1 U.S. weekly interest over time on six exercise items 215\u003c\/p\u003e \u003cp\u003e6.6.1.1 Choose a best VAR\/VARMA model 216\u003c\/p\u003e \u003cp\u003e6.6.1.2 Finding a VARMA-ARCH\/GARCH model 218\u003c\/p\u003e \u003cp\u003e6.6.1.3 The fitted values from VAR(1)-ARCH(1) model 221\u003c\/p\u003e \u003cp\u003e6.6.2 Daily log-returns of the SP 500 index and three financial stocks 222\u003c\/p\u003e \u003cp\u003e6.6.3 The analysis of the Dow Jones Industrial Average of 30 stocks 225\u003c\/p\u003e \u003cp\u003eSoftware code 229\u003c\/p\u003e \u003cp\u003eProjects 234\u003c\/p\u003e \u003cp\u003eReferences 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Repeated Measurements 237\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 237\u003c\/p\u003e \u003cp\u003e7.2 Multivariate analysis of variance 239\u003c\/p\u003e \u003cp\u003e7.2.1 Test treatment effects 239\u003c\/p\u003e \u003cp\u003e7.2.2 Empirical Example I – First analysis on body weight of rats under three different treatments 241\u003c\/p\u003e \u003cp\u003e7.3 Models utilizing time series structure 243\u003c\/p\u003e \u003cp\u003e7.3.1 Fixed effects model 243\u003c\/p\u003e \u003cp\u003e7.3.2 Some common variance–covariance structures 247\u003c\/p\u003e \u003cp\u003e7.3.3 Empirical Example II – Further analysis on body weight of rats under three different treatments 250\u003c\/p\u003e \u003cp\u003e7.3.4 Random effects and mixed effects models 252\u003c\/p\u003e \u003cp\u003e7.4 Nested random effects model 253\u003c\/p\u003e \u003cp\u003e7.5 Further generalization and remarks 254\u003c\/p\u003e \u003cp\u003e7.6 Another empirical example – the oral condition of neck cancer patients 255\u003c\/p\u003e \u003cp\u003eSoftware code 257\u003c\/p\u003e \u003cp\u003eProjects 258\u003c\/p\u003e \u003cp\u003eReferences 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Space–Time Series Models 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 261\u003c\/p\u003e \u003cp\u003e8.2 Space–time autoregressive integrated moving average (STARIMA) models 262\u003c\/p\u003e \u003cp\u003e8.2.1 Spatial weighting matrix 262\u003c\/p\u003e \u003cp\u003e8.2.2 STARIMA models 265\u003c\/p\u003e \u003cp\u003e8.2.3 STARMA models 266\u003c\/p\u003e \u003cp\u003e8.2.4 ST-ACF and ST-PACF 267\u003c\/p\u003e \u003cp\u003e8.3 Generalized space–time autoregressive integrated moving average (GSTARIMA) models 272\u003c\/p\u003e \u003cp\u003e8.4 Iterative model building of STARMA and GSTARMA models 273\u003c\/p\u003e \u003cp\u003e8.5 Empirical examples 273\u003c\/p\u003e \u003cp\u003e8.5.1 Vehicular theft data 273\u003c\/p\u003e \u003cp\u003e8.5.2 The annual U.S. labor force count 279\u003c\/p\u003e \u003cp\u003e8.5.3 U.S. yearly sexually transmitted disease data 281\u003c\/p\u003e \u003cp\u003eSoftware code 289\u003c\/p\u003e \u003cp\u003eProjects 298\u003c\/p\u003e \u003cp\u003eReferences 298\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Multivariate Spectral Analysis of Time Series 301\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 301\u003c\/p\u003e \u003cp\u003e9.2 Spectral representations of multivariate time series processes 304\u003c\/p\u003e \u003cp\u003e9.3 The estimation of the spectral density matrix 309\u003c\/p\u003e \u003cp\u003e9.3.1 The smoothed spectrum matrix 309\u003c\/p\u003e \u003cp\u003e9.3.2 Multitaper smoothing 313\u003c\/p\u003e \u003cp\u003e9.3.3 Smoothing spline 315\u003c\/p\u003e \u003cp\u003e9.3.4 Bayesian method 316\u003c\/p\u003e \u003cp\u003e9.3.5 Penalized Whittle likelihood 317\u003c\/p\u003e \u003cp\u003e9.3.6 VARMA spectral estimation 318\u003c\/p\u003e \u003cp\u003e9.4 Empirical examples of stationary vector time series 320\u003c\/p\u003e \u003cp\u003e9.4.1 Sample spectrum 320\u003c\/p\u003e \u003cp\u003e9.4.2 Bayesian method 325\u003c\/p\u003e \u003cp\u003e9.4.3 Penalized Whittle likelihood method 327\u003c\/p\u003e \u003cp\u003e9.4.4 Example of VAR spectrum estimation 327\u003c\/p\u003e \u003cp\u003e9.5 Spectrum analysis of a nonstationary vector time series 329\u003c\/p\u003e \u003cp\u003e9.5.1 Introduction 329\u003c\/p\u003e \u003cp\u003e9.5.2 Spectrum representations of a nonstationary multivariate process 331\u003c\/p\u003e \u003cp\u003e9.5.2.1 Time-varying autoregressive model 332\u003c\/p\u003e \u003cp\u003e9.5.2.2 Smoothing spline ANOVA model 333\u003c\/p\u003e \u003cp\u003e9.5.2.3 Piecewise vector autoregressive model 334\u003c\/p\u003e \u003cp\u003e9.5.2.4 Bayesian methods 336\u003c\/p\u003e \u003cp\u003e9.6 Empirical spectrum example of nonstationary vector time series 337\u003c\/p\u003e \u003cp\u003eSoftware code 341\u003c\/p\u003e \u003cp\u003eProjects 434\u003c\/p\u003e \u003cp\u003eReferences 435\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Dimension Reduction in High-Dimensional Multivariate Time Series Analysis 437\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 437\u003c\/p\u003e \u003cp\u003e10.2 Existing methods 438\u003c\/p\u003e \u003cp\u003e10.2.1 Regularization methods 439\u003c\/p\u003e \u003cp\u003e10.2.1.1 The lasso method 439\u003c\/p\u003e \u003cp\u003e10.2.1.2 The lag-weighted lasso method 440\u003c\/p\u003e \u003cp\u003e10.2.1.3 The hierarchical vector autoregression (HVAR) method 440\u003c\/p\u003e \u003cp\u003e10.2.2 The space–time AR (STAR) model 442\u003c\/p\u003e \u003cp\u003e10.2.3 The model-based cluster method 443\u003c\/p\u003e \u003cp\u003e10.2.4 The factor analysis 443\u003c\/p\u003e \u003cp\u003e10.3 The proposed method for high-dimension reduction 444\u003c\/p\u003e \u003cp\u003e10.4 Simulation studies 446\u003c\/p\u003e \u003cp\u003e10.4.1 Scenario 1 446\u003c\/p\u003e \u003cp\u003e10.4.2 Scenario 2 449\u003c\/p\u003e \u003cp\u003e10.4.3 Scenario 3 449\u003c\/p\u003e \u003cp\u003e10.5 Empirical examples 452\u003c\/p\u003e \u003cp\u003e10.5.1 The macroeconomic time series 452\u003c\/p\u003e \u003cp\u003e10.5.2 The yearly U.S. STD data 457\u003c\/p\u003e \u003cp\u003e10.6 Further discussions and remarks 459\u003c\/p\u003e \u003cp\u003e10.6.1 More on clustering 459\u003c\/p\u003e \u003cp\u003e10.6.2 Forming aggregate data through both time domain and frequency domain clustering 461\u003c\/p\u003e \u003cp\u003e10.6.2.1 Example of time domain clustering 461\u003c\/p\u003e \u003cp\u003e10.6.2.2 Example of frequency domain clustering 461\u003c\/p\u003e \u003cp\u003e10.6.2.2.1 Clustering using similarity measures 463\u003c\/p\u003e \u003cp\u003e10.6.2.2.2 Clustering by subjective observation 463\u003c\/p\u003e \u003cp\u003e10.6.2.2.3 Hierarchical clustering 463\u003c\/p\u003e \u003cp\u003e10.6.2.2.4 Nonhierarchical clustering using the K-means method 463\u003c\/p\u003e \u003cp\u003e10.6.3 The specification of aggregate matrix and its associated aggregate dimension 466\u003c\/p\u003e \u003cp\u003e10.6.4 Be aware of other forms of aggregation 466\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10.A Appendix: Parameter Estimation Results of Various Procedures 467\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.A.1 Further details of the macroeconomic time series 467\u003c\/p\u003e \u003cp\u003e10.A.1.1 VAR(1) 467\u003c\/p\u003e \u003cp\u003e10.A.1.2 Lasso 468\u003c\/p\u003e \u003cp\u003e10.A.1.3 Componentwise 470\u003c\/p\u003e \u003cp\u003e10.A.1.4 Own-other 471\u003c\/p\u003e \u003cp\u003e10.A.1.5 Elementwise 473\u003c\/p\u003e \u003cp\u003e10.A.1.6 The factor model 475\u003c\/p\u003e \u003cp\u003e10.A.1.7 The model-based cluster 475\u003c\/p\u003e \u003cp\u003e10.A.1.8 The proposed method 477\u003c\/p\u003e \u003cp\u003e10.A.2 Further details of the STD time series 478\u003c\/p\u003e \u003cp\u003e10.A.2.1 VAR 478\u003c\/p\u003e \u003cp\u003e10.A.2.2 Lasso 478\u003c\/p\u003e \u003cp\u003e10.A.2.3 Componentwise 479\u003c\/p\u003e \u003cp\u003e10.A.2.4 Own-other 481\u003c\/p\u003e \u003cp\u003e10.A.2.5 Elementwise 482\u003c\/p\u003e \u003cp\u003e10.A.2.6 The STAR model 484\u003c\/p\u003e \u003cp\u003e10.A.2.7 The factor model 486\u003c\/p\u003e \u003cp\u003e10.A.2.8 The model-based cluster 487\u003c\/p\u003e \u003cp\u003e10.A.2.9 The proposed method 488\u003c\/p\u003e \u003cp\u003eSoftware code 490\u003c\/p\u003e \u003cp\u003eProjects 505\u003c\/p\u003e \u003cp\u003eReferences 506\u003c\/p\u003e \u003cp\u003eAuthor index 509\u003c\/p\u003e \u003cp\u003eSubject index 515\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52173825311000,"sku":"9781119502852","price":78.38,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119502852.jpg?v=1781173696","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/multivariate-time-series-analysis-and-applications-hardback-9781119502852","provider":"Freshly Printed Books","version":"1.0","type":"link"}