Problems Of Time Series Analysis

Author : NERLOVE
Publisher : Birkhäuser
Page : 104 pages
File Size : 52,9 Mb
Release : 2012-07-04
Category : Computers
ISBN : 146159927X

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Problems of Time Series Analysis by NERLOVE Pdf

The last decade has witnessed an increased interest in time series analysis. Non-parametric methods like spectral and cross spectral analysis are used to discover regularities in individual time series, re lationships between specific components of different time series and leads or lags between those series. Box-Jenkins procedures for the pa rametric estimation of autoregressive-moving average schemes be long nowadays to the standard equipment of a computer center. In economics this revival of time series analysis has led to numer ous empirical studies on optimal seasonal adjustment procedures, the behavior of prices, production, employment etc. More recently, Box Jenkins methods form an integral part for tests on the efficiency of markets, the effectiveness of monetary and fiscal policies and for the study of the röle of different assumptions on the formation of expec tations. This volume comprehends aseries of lectures which deal with var ious topics of time series analysis delivered during the wintersemester 1978/79 at the faculty of economics and statistics. The collection be gins with a paper by M. Nerlove introducing the concept of unob served components. Theoretical results are illustrated by examples se ries on prices of steers, heifers, cows and milk, of cattle and for time hog slaughter, of industrial production and male unemployment. The study by S. Heiler considers a mixed model with a linear regression part and a regular residual process for the prediction of economic processes when additional information is available.

Author : Aileen Nielsen
Publisher : O'Reilly Media
Page : 500 pages
File Size : 40,7 Mb
Release : 2019-09-20
Category : Computers
ISBN : 9781492041627

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Practical Time Series Analysis by Aileen Nielsen Pdf

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance

Author : Rob J Hyndman,George Athanasopoulos
Publisher : OTexts
Page : 380 pages
File Size : 48,8 Mb
Release : 2018-05-08
Category : Business & Economics
ISBN : 9780987507112

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Forecasting: principles and practice by Rob J Hyndman,George Athanasopoulos Pdf

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

Author : Christopher Chatfield
Publisher : Springer
Page : 277 pages
File Size : 54,8 Mb
Release : 2013-12-01
Category : Mathematics
ISBN : 9781489929259

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The Analysis of Time Series: Theory and Practice by Christopher Chatfield Pdf

Time-series analysis is an area of statistics which is of particular interest at the present time. Time series arise in many different areas, ranging from marketing to oceanography, and the analysis of such series raises many problems of both a theoretical and practical nature. I first became interested in the subject as a postgraduate student at Imperial College, when I attended a stimulating course of lectures on time-series given by Dr. (now Professor) G. M. Jenkins. The subject has fascinated me ever since. Several books have been written on theoretical aspects of time-series analysis. The aim of this book is to provide an introduction to the subject which bridges the gap between theory and practice. The book has also been written to make what is rather a difficult subject as understandable as possible. Enough theory is given to introduce the concepts of time-series analysis and to make the book mathematically interesting. In addition, practical problems are considered so as to help the reader tackle the analysis of real data. The book assumes a knowledge of basic probability theory and elementary statistical inference (see Appendix III). The book can be used as a text for an undergraduate or postgraduate course in time-series, or it can be used for self tuition by research workers. Throughout the book, references are usually given to recent readily accessible books and journals rather than to the original attributive references. Wold's (1965) bibliography contains many time series references published before 1959.

Author : Wayne A. Woodward,Henry L. Gray,Alan C. Elliott
Publisher : CRC Press
Page : 635 pages
File Size : 49,6 Mb
Release : 2017-02-17
Category : Mathematics
ISBN : 9781498734271

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Applied Time Series Analysis with R by Wayne A. Woodward,Henry L. Gray,Alan C. Elliott Pdf

Virtually any random process developing chronologically can be viewed as a time series. In economics closing prices of stocks, the cost of money, the jobless rate, and retail sales are just a few examples of many. Developed from course notes and extensively classroom-tested, Applied Time Series Analysis with R, Second Edition includes examples across a variety of fields, develops theory, and provides an R-based software package to aid in addressing time series problems in a broad spectrum of fields. The material is organized in an optimal format for graduate students in statistics as well as in the natural and social sciences to learn to use and understand the tools of applied time series analysis. Features Gives readers the ability to actually solve significant real-world problems Addresses many types of nonstationary time series and cutting-edge methodologies Promotes understanding of the data and associated models rather than viewing it as the output of a "black box" Provides the R package tswge available on CRAN which contains functions and over 100 real and simulated data sets to accompany the book. Extensive help regarding the use of tswge functions is provided in appendices and on an associated website. Over 150 exercises and extensive support for instructors The second edition includes additional real-data examples, uses R-based code that helps students easily analyze data, generate realizations from models, and explore the associated characteristics. It also adds discussion of new advances in the analysis of long memory data and data with time-varying frequencies (TVF).

Author : Douglas C. Montgomery,Cheryl L. Jennings,Murat Kulahci
Publisher : John Wiley & Sons
Page : 327 pages
File Size : 47,7 Mb
Release : 2011-09-20
Category : Mathematics
ISBN : 9781118211502

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Introduction to Time Series Analysis and Forecasting by Douglas C. Montgomery,Cheryl L. Jennings,Murat Kulahci Pdf

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including: Regression-based methods, heuristic smoothing methods, and general time series models Basic statistical tools used in analyzing time series data Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performance over time Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares Exponential smoothing techniques for time series with polynomial components and seasonal data Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab, JMP, and SAS software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series courses at the advanced undergraduate and beginning graduate levels. The book also serves as an indispensable reference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences.

Author : Dinesh C. S. Bisht,Mangey Ram
Publisher : CRC Press
Page : 191 pages
File Size : 54,7 Mb
Release : 2022-11-30
Category : Science
ISBN : 9781000793819

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Computational Intelligence-based Time Series Analysis by Dinesh C. S. Bisht,Mangey Ram Pdf

The sequential analysis of data and information gathered from past to present is called time series analysis. Time series data are of high dimension, large size and updated continuously. A time series depends on various factors like trend, seasonality, cycle and irregular data set, and is basically a series of data points well-organized in time. Time series forecasting is a significant area of machine learning. There are various prediction problems that are time-dependent and these problems can be handled through time series analysis. Computational intelligence (CI) is a developing computing approach for the forthcoming several years. CI gives the litheness to model the problem according to given requirements. It helps to find swift solutions to the problems arising in numerous disciplines. These methods mimic human behavior. The main objective of CI is to develop intelligent machines to provide solutions to real world problems, which are not modelled or are too difficult to model mathematically. This book aims to cover the recent advances in time series and applications of CI for time series analysis.

Author : William W. S. Wei
Publisher : Pearson
Page : 648 pages
File Size : 47,5 Mb
Release : 2018-03-14
Category : Time-series analysis
ISBN : 0134995368

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Time Series Analysis Univariate and Multivariate Methods by William W. S. Wei Pdf

With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications. Time Series Analysis is a thorough introduction to both time-domain and frequency-domain analyses of univariate and multivariate time series methods, with coverage of the most recently developed techniques in the field.

Author : Robert H. Shumway,David S. Stoffer
Publisher : Unknown
Page : 568 pages
File Size : 47,7 Mb
Release : 2014-01-15
Category : Electronic
ISBN : 1475732627

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Time Series Analysis and Its Applications by Robert H. Shumway,David S. Stoffer Pdf

Author : Henrik Madsen
Publisher : CRC Press
Page : 390 pages
File Size : 40,6 Mb
Release : 2007-11-28
Category : Mathematics
ISBN : 9781420059687

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Time Series Analysis by Henrik Madsen Pdf

With a focus on analyzing and modeling linear dynamic systems using statistical methods, Time Series Analysis formulates various linear models, discusses their theoretical characteristics, and explores the connections among stochastic dynamic models. Emphasizing the time domain description, the author presents theorems to highlight the most

Author : Douglas C. Montgomery,Cheryl L. Jennings,Murat Kulahci
Publisher : John Wiley & Sons
Page : 672 pages
File Size : 54,5 Mb
Release : 2015-04-21
Category : Mathematics
ISBN : 9781118745151

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Introduction to Time Series Analysis and Forecasting by Douglas C. Montgomery,Cheryl L. Jennings,Murat Kulahci Pdf

Praise for the First Edition "...[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics." -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts. Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both popular and modern time series methodologies as well as an introduction to Bayesian methods in forecasting. Introduction to Time Series Analysis and Forecasting, Second Edition also includes: Over 300 exercises from diverse disciplines including health care, environmental studies, engineering, and finance More than 50 programming algorithms using JMP®, SAS®, and R that illustrate the theory and practicality of forecasting techniques in the context of time-oriented data New material on frequency domain and spatial temporal data analysis Expanded coverage of the variogram and spectrum with applications as well as transfer and intervention model functions A supplementary website featuring PowerPoint® slides, data sets, and select solutions to the problems Introduction to Time Series Analysis and Forecasting, Second Edition is an ideal textbook upper-undergraduate and graduate-levels courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.

Author : Olga Valenzuela,Fernando Rojas,Luis Javier Herrera,Héctor Pomares,Ignacio Rojas
Publisher : Springer Nature
Page : 460 pages
File Size : 46,9 Mb
Release : 2020-11-20
Category : Business & Economics
ISBN : 9783030562199

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Theory and Applications of Time Series Analysis by Olga Valenzuela,Fernando Rojas,Luis Javier Herrera,Héctor Pomares,Ignacio Rojas Pdf

This book presents a selection of peer-reviewed contributions on the latest advances in time series analysis, presented at the International Conference on Time Series and Forecasting (ITISE 2019), held in Granada, Spain, on September 25-27, 2019. The first two parts of the book present theoretical contributions on statistical and advanced mathematical methods, and on econometric models, financial forecasting and risk analysis. The remaining four parts include practical contributions on time series analysis in energy; complex/big data time series and forecasting; time series analysis with computational intelligence; and time series analysis and prediction for other real-world problems. Given this mix of topics, readers will acquire a more comprehensive perspective on the field of time series analysis and forecasting. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics.

Author : Changquan Huang,Alla Petukhina
Publisher : Springer
Page : 0 pages
File Size : 40,6 Mb
Release : 2023-10-20
Category : Mathematics
ISBN : 3031135865

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Applied Time Series Analysis and Forecasting with Python by Changquan Huang,Alla Petukhina Pdf

This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems. It covers not only common statistical approaches and time series models, including ARMA, SARIMA, VAR, GARCH and state space and Markov switching models for (non)stationary, multivariate and financial time series, but also modern machine learning procedures and challenges for time series forecasting. Providing an organic combination of the principles of time series analysis and Python programming, it enables the reader to study methods and techniques and practice writing and running Python code at the same time. Its data-driven approach to analyzing and modeling time series data helps new learners to visualize and interpret both the raw data and its computed results. Primarily intended for students of statistics, economics and data science with an undergraduate knowledge of probability and statistics, the book will equally appeal to industry professionals in the fields of artificial intelligence and data science, and anyone interested in using Python to solve time series problems.

Author : Ben Auffarth
Publisher : Packt Publishing Ltd
Page : 371 pages
File Size : 51,9 Mb
Release : 2021-10-29
Category : Computers
ISBN : 9781801816106

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Machine Learning for Time-Series with Python by Ben Auffarth Pdf

Get better insights from time-series data and become proficient in model performance analysis Key FeaturesExplore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description The Python time-series ecosystem is huge and often quite hard to get a good grasp on, especially for time-series since there are so many new libraries and new models. This book aims to deepen your understanding of time series by providing a comprehensive overview of popular Python time-series packages and help you build better predictive systems. Machine Learning for Time-Series with Python starts by re-introducing the basics of time series and then builds your understanding of traditional autoregressive models as well as modern non-parametric models. By observing practical examples and the theory behind them, you will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering. This book will also guide you in matching the right model to the right problem by explaining the theory behind several useful models. You'll also have a look at real-world case studies covering weather, traffic, biking, and stock market data. By the end of this book, you should feel at home with effectively analyzing and applying machine learning methods to time-series. What you will learnUnderstand the main classes of time series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning modelsBecome familiar with many libraries like Prophet, XGboost, and TensorFlowWho this book is for This book is ideal for data analysts, data scientists, and Python developers who want instantly useful and practical recipes to implement today, and a comprehensive reference book for tomorrow. Basic knowledge of the Python Programming language is a must, while familiarity with statistics will help you get the most out of this book.

Author : Wilfredo Palma
Publisher : John Wiley & Sons
Page : 616 pages
File Size : 42,8 Mb
Release : 2016-04-29
Category : Mathematics
ISBN : 9781118634233

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Time Series Analysis by Wilfredo Palma Pdf

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. The book focuses on the treatment of univariate time series by illustrating a number of well-known models such as ARMA and ARIMA. Providing contemporary coverage, the book features several useful and newlydeveloped techniques such as weak and strong dependence, Bayesian methods, non-Gaussian data, local stationarity, missing values and outliers, and threshold models. Time Series Analysis includes practical applications of time series methods throughout, as well as: Real-world examples and exercise sets that allow readers to practice the presented methods and techniques Numerous detailed analyses of computational aspects related to the implementation of methodologies including algorithm efficiency, arithmetic complexity, and process time End-of-chapter proposed problems and bibliographical notes to deepen readers’ knowledge of the presented material Appendices that contain details on fundamental concepts and select solutions of the problems implemented throughout A companion website with additional data fi les and computer codes Time Series Analysis is an excellent textbook for undergraduate and beginning graduate-level courses in time series as well as a supplement for students in advanced statistics, mathematics, economics, finance, engineering, and physics. The book is also a useful reference for researchers and practitioners in time series analysis, econometrics, and finance. Wilfredo Palma, PhD, is Professor of Statistics in the Department of Statistics at Pontificia Universidad Católica de Chile. He has published several refereed articles and has received over a dozen academic honors and awards. His research interests include time series analysis, prediction theory, state space systems, linear models, and econometrics. He is the author of Long-Memory Time Series: Theory and Methods, also published by Wiley.