| John Wiley & Sons Inc

237,860원  | 20160331  | 9781118634325

A modern and accessible guide to the analysis of introductory time series data Featuring an organized and self-contained guide, Time Series Analysis provides a broad introduction to the most fundamental methodologies and techniques of time series analysis.

Wei, William W. S.  | Pearson

0원  | 20050708  | 9780321322166

1: Overview 1.1 Introduction 1.2 Examples and Scope of This Book 2: Fundamental Concepts 2.1 Stochastic Processes 2.2 The Autocovariance and Autocorrelation Functions 2.3 The Partial Autocorrelation Function 2.4 White Noise Processes 2.5 Estimation of the Mean, Autocovariances, and Autocorrelations 2.5.1 Sample Mean 2.5.2 Sample Autocovariance Function 2.5.3 Sample Autocorrelation Function 2.5.4 Sample Partial Autocorrelation Function 2.6 Moving Average and Autoregressive Representations of Time Series Processes 2.7 Linear Difference Equations 3: Stationary Time Series Models 3.1 Autoregressive Processes 3.1.1 The First-Order Autoregressive AR(1) Process 3.1.2 The Second-Order Autoregressive AR(2) Process 3.1.3 The Generalpth-Order Autoregressive AR(p) Process 3.2 Moving Average Processes 3.2.1 The First-Order Moving Average MA(1) Process 3.2.2 The Second-Order Moving Average MA(2) Process 3.2.3 The Generalqth-Order Moving Average MA(q) Process 3.3 The Dual Relationship Between AR(p) and MA(q) Processes 3.4 Autoregressive Moving Average ARMA(p, q) Processes 3.4.1 The General Mixed ARMA(p, q) Process 3.4.2 The ARMA(1, 1) Process 4: Nonstationary Time Series Models 4.1 Nonstationarity in the Mean 4.1.1 Deterministic Trend Models 4.1.2 Stochastic Trend Models and Differencing 4.2 Autoregr

 | CRC Press

139,590원  | 20250413  | 9781032418865

The concept of deep machine learning is easier to understand by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the cycle-to-cycle variations.

George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, Greta M. Ljung  | Wiley

72,000원  | 20170201  | 9781118675021

Praise for the Fourth Edition “The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control." - Mathematical Reviews Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series.

 | KS OmniScriptum Publishing

93,070원  | 20250724  | 9786207996124

This book explores the importance of accurate rainfall forecasting for water resource management, agriculture, and disaster preparedness. It presents a comparative analysis of two forecasting models-Support Vector Regression (SVR) and Seasonal Auto Regressive Integrated Moving Average (SARIMA)-using historical rainfall data from 2008 to 2021 to predict trends from 2022 to 2026.

James D. Hamilton  | Princeton

89,000원  | 20130701  | 9780691042893

The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data.Time Series Analysisfills an important need for a textbook that integrates economic theory, econometrics, and new results. The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.

Weisinger, Corey, Widmann, Maarit, Tonini, Daniele  | Packt Publishing

43,000원  | 20220819  | 9781803232065

This book will take you through time series analysis techniques and algorithms, from both statistical and machine learning domains. It will introduce you to the KNIME Analytics Platform and its time series extension for implementing practical solutions for demand prediction, signal classification, signal forecasting, and anomaly detection.

 | River Publishers

95,270원  | 20241021  | 9788770042574

The main objective of CI is to develop intelligent machines to provide solutions to real world problems, which are not modelled or too difficult to model mathematically. This book aims to cover the recent advances in time series and applications of CI for time series analysis.

양동령  | 퍼플

0원  | 20211130  | 9788924087499

This book intends to provide mathematical background for the practitioners who apply the concept of time-series into real-world cases. One of the most hazardous situations occurs when people blindly apply formulas and concepts without in-depth study about essential assumptions. Understanding the assumptions and the way how the formulas are derived enables practitioners to modify the concepts in textbooks, when they face new realities to which the old formulas are not applicable anymore.

 | Hutson Street Press

34,580원  | 20250522  | 9781025122106

Multiple Model Adaptive Estimation (MMAE) is a Bayesian technique that applies a bank of Kalman filters to predict future observations. Each Kalman filter is based on a different set of parameters and hence produces different residuals. The likelihood of each Kalman filter's prediction is determined by a magnitude of the residuals. Since some researchers have obtained good forecasts using a single Kalman filter, we tested MMAE's ability to make time series predictions.

 | Hutson Street Press

58,300원  | 20250522  | 9781025118666

Multiple Model Adaptive Estimation (MMAE) is a Bayesian technique that applies a bank of Kalman filters to predict future observations. Each Kalman filter is based on a different set of parameters and hence produces different residuals. The likelihood of each Kalman filter's prediction is determined by a magnitude of the residuals. Since some researchers have obtained good forecasts using a single Kalman filter, we tested MMAE's ability to make time series predictions.

양동령  | 퍼플

45,500원  | 20231201  | 9788924117547

As a former practitioner and currently a researcher in the field of social science, the author had hard time catching up mathematical and statistical tools, the backbone essential in yielding acceptable academic research output. Such personal deficiency motivates the writing of this book. Under the circumstance that there already exists a sufficiently large volume of the textbooks dealing with time-series analysis at different levels, this book tries to find its contribution in the following sense. First, this book aims to explain the topic of time-series analysis in a single volume such that even the audience with no prior knowledge about statistics or mathematics in advanced level can understand the topic. Whenever possible, this book presents mathematical background necessary to understand specific statistical topics. Second, this book provides line-by-line calculation during deriving most of the formulas. During the calculation, the author put different colors into texts so that the audience can get intuitive insight regarding how seemingly complex calculations flow in detail. Whenever possible, the author tries to write down the intuition of certain subjects; such intuition helps understanding more easily what matters for study subjects.

 | Cambridge

36,000원  | 19990701  | 9780521653879

Rami Krispin  | Packt Publishing

45,000원  | 20190531  | 9781788629157

This book introduces you to time series analysis and forecasting with R; this is one of the key fields in statistical programming and includes techniques for analyzing data to extract meaningful insights. You will explore methods, such as prediction with time series analysis, and identify the relationship between each data point in the series.

Madsen, Henrik  | CRC

70,000원  | 20071128  | 9781420059670

Emphasizes basic methods for modeling linear dynamic systems. This book presents an understanding of basic concepts, such as multivariate random variables, stochastic processes, and regression-based methods. It covers topics that include spectral analysis, state space models, and recursive estimation.