Time - series analysis of road safety trends aggregated at national level in europe for 2000-2010 Politechnika Gdańska

INNE

"Time-series analysis of road safety trends aggregated at national level in Europe for 2000

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Time Series Analysis John Wiley & Sons Inc

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Since publication of the first edition in 1970, "Time SeriesAnalysis" has served as one of the most influential andprominent works on the subject. This new edition maintains itsbalanced presentation of the tools for modeling and analyzing timeseries and also introduces the latest developments that haveoccurred in the field over the past decade through applicationsfrom areas such as business, finance, and engineering. Along withthe addition of a new co-author, Greta Ljung, a student of GeorgeBox, the Fifth Edition provides a clearly written exploration ofthe key methods for building, classifying, testing, and analyzingstochastic models for time series as well as their use in fiveimportant areas of application: forecasting; determining thetransfer function of a system; modeling the effects of interventionevents; developing multivariate dynamic models; and designingsimple control schemes. New to this edition are more exercises;streamlined chapter introductions; updated examples and references;increased coverage of multivariate time series and their use infinancial applications; SV models, MCMC based inference, andBayesian Network modeling; and the incorporation of the'R' freeware language.

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Nonlinear Time Series Analysis Cambridge University Press

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The paradigm of deterministic chaos has influenced thinking in many fields of science. Chaotic systems show rich and surprising mathematical structures. In the applied sciences, deterministic chaos provides a striking explanation for irregular behaviour and anomalies in systems which do not seem to be inherently stochastic. The most direct link between chaos theory and the real world is the analysis of time series from real systems in terms of nonlinear dynamics. Experimental technique and data analysis have seen such dramatic progress that, by now, most fundamental properties of nonlinear dynamical systems have been observed in the laboratory. Great efforts are being made to exploit ideas from chaos theory wherever the data displays more structure than can be captured by traditional methods. Problems of this kind are typical in biology and physiology but also in geophysics, economics, and many other sciences.

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Introduction to Modern Time Series Analysis Springer-Verlag Berlin and Heidelberg GmbH & Co....

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This book presents modern methods of time series econometrics and their applications to macroeconomics and finance. It includes numerous examples and analyses based on real economic data.

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Hands-On Time Series Analysis with R Packt Publishing Limited

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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.

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Analysis of Directional Time Series: Applications to Wind Speed and Direction Springer-Verlag New York Inc.

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Given a series of wind speeds and directions from the port of Fremantle the aim of this monograph is to detect general weather patterns and seasonal characteristics. To separate the daily land and sea breeze cycle and other short-term disturbances from the general wind, the series is divided into a daily and a longer term, synoptic component. The latter is related to the atmospheric pressure field, while the former is studied in order i) to isolate particular short-term events such as calms, storms and oscillating winds, and ii) to determine the land and sea breeze cycle which dominates the weather pattern for most of the year. All these patterns are described in detail and are related to the synoptic component of the data. Two time series models for directional data and a new measure of angular association are introduced to provide the basis for certain parts of the analysis.

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Applied Time Series Analysis and Forecasting with Python Springer, Berlin

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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.

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Applied Time Series Analysis with R Taylor & Francis Ltd

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Time Series Analysis with Python Cookbook Packt Publishing

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Theory and Applications of Time Series Analysis and Forecasting Springer, Berlin

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This book presents a selection of peer-reviewed contributions on the latest developments in time series analysis and forecasting, presented at the 7th International Conference on Time Series and Forecasting, ITISE 2021, held in Gran Canaria, Spain, July 19-21, 2021. It is divided into four parts. The first part addresses general modern methods and theoretical aspects of time series analysis and forecasting, while the remaining three parts focus on forecasting methods in econometrics, time series forecasting and prediction, and numerous other real-world applications. Covering a broad range of topics, the book will give readers a modern perspective on the subject.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.

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Time Series Analysis by State Space Methods Oxford University Press

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This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series. Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations. Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.

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Introduction to Modern Time Series Analysis Springer-Verlag Berlin and Heidelberg GmbH & Co....

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This book presents modern methods of time series econometrics and their applications to macroeconomics and finance. It includes numerous examples and analyses based on real economic data.

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Multivariate Time Series Analysis and Applications John Wiley and Sons Ltd

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Due to high-speed internet and the power and speed of the new generation of computers, a researcher now faces somevery challenging phenomena and must deal with an ever-increasing amount of data.

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Practical Time Series Analysis O'Reilly Media, Inc, USA

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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 commo

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Analysis of Financial Time Series 3e John Wiley & Sons Inc

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This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.§The author begins with basic characteristics of financial time series data before covering three main topics:§Analysis and application of univariate financial time series§The return series of multiple assets§Bayesian inference in finance methods§Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets.§The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

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