Exploratory Causal Analysis with Time Series Data

Exploratory Causal Analysis with Time Series Data
Author: James M. McCracken
Publisher: Springer Nature
Total Pages: 133
Release: 2022-06-01
Genre: Computers
ISBN: 3031019091

Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal structures in time series data sets and is characterized by a myopic goal to determine which data series from a given set of series might be seen as the primary driver. In this work, ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.

Data Analysis for Business, Economics, and Policy

Data Analysis for Business, Economics, and Policy
Author: Gábor Békés
Publisher: Cambridge University Press
Total Pages: 741
Release: 2021-05-06
Genre: Business & Economics
ISBN: 1108483011

A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data.

Causality in Time Series: Challenges in Machine Learning

Causality in Time Series: Challenges in Machine Learning
Author: Florin Popescu
Publisher:
Total Pages: 152
Release: 2013-06
Genre: Computers
ISBN: 9780971977754

This volume in the Challenges in Machine Learning series gathers papers from the Mini Symposium on Causality in Time Series, which was part of the Neural Information Processing Systems (NIPS) confernce in 2009 in Vancouver, Canada. These papers present state-of-the-art research in time-series causality to the machine learning community, unifying methodological interests in the various communities that require such inference.

Practical Time Series Analysis

Practical Time Series Analysis
Author: Aileen Nielsen
Publisher: O'Reilly Media
Total Pages: 500
Release: 2019-09-20
Genre: Computers
ISBN: 1492041629

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

Spectral Analysis of Time-series Data

Spectral Analysis of Time-series Data
Author: Rebecca M. Warner
Publisher: Guilford Press
Total Pages: 244
Release: 1998-05-22
Genre: Social Science
ISBN: 9781572303386

This book provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. It is written both for researchers and students new to the area and for those who have already collected time-series data but wish to learn new ways of understanding and presenting them. Facilitating the interpretation of observations of behavior, physiology, mood, perceptual threshold, social indicator variables, and other responses, the book focuses on practical applications and requires much less mathematical background than most comparable texts. Using real data sets and currently available software (SPSS for Windows), the author employs extensive examples to clarify key concepts. Topics covered include research design issues, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series. Also considered are theoretical questions, problems of interpretation, and potential sources of artifact.

Time Series Analysis and Its Applications

Time Series Analysis and Its Applications
Author: Robert H. Shumway
Publisher: Springer Science & Business Media
Total Pages: 560
Release: 2013-03-14
Genre: Mathematics
ISBN: 1475732619

A balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using non-trivial data illustrate solutions to problems, such as evaluating pain perception experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty. Although designed as a text for graduate level students in statistics and the physical, biological and social sciences, some parts of the book will also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels, and the material has been updated by adding modern developments involving categorical time series analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. The book is supplemented by data and an exploratory time series analysis program ASTSA for Windows that can be downloaded from the Web as freeware.

Causal Analysis

Causal Analysis
Author: Lawrence R. James
Publisher: SAGE Publications, Incorporated
Total Pages: 184
Release: 1982-10
Genre: Business & Economics
ISBN:

This book focuses specifically on confirmatory analysis - a quantitative technique used to illuminate causal relationships among organizational phenomena. The authors outline the conditions that must be met if causal inferences are to be drawn from nonexperimental data, and offer new tests for determining whether data meet those conditions. While analytic models and techniques of confirmatory analysis are stressed here, the authors also emphasize the importance of strong, well-developed theory as a prerequisite to the appropriate application of these powerful (but easily misused) tools.

Design and Analysis of Time Series Experiments

Design and Analysis of Time Series Experiments
Author: Richard McCleary
Publisher: Oxford University Press
Total Pages: 393
Release: 2017
Genre: Business & Economics
ISBN: 0190661569

Design and Analysis of Time Series Experiments develops methods and models for analysis and interpretation of time series experiments while also addressing recent developments in causal modeling. Unlike other time series texts, it integrates the statistical issues of design, estimation, and interpretation with foundational validity issues. Drawing on examples from criminology, economics, education, pharmacology, public policy, program evaluation, public health, and psychology, this text addresses researchers and graduate students in a wide range of the behavioral, biomedical, and social sciences.

Handbook of Time Series Analysis

Handbook of Time Series Analysis
Author: Björn Schelter
Publisher: John Wiley & Sons
Total Pages: 514
Release: 2006-12-13
Genre: Science
ISBN: 3527609512

This handbook provides an up-to-date survey of current research topics and applications of time series analysis methods written by leading experts in their fields. It covers recent developments in univariate as well as bivariate and multivariate time series analysis techniques ranging from physics' to life sciences' applications. Each chapter comprises both methodological aspects and applications to real world complex systems, such as the human brain or Earth's climate. Covering an exceptionally broad spectrum of topics, beginners, experts and practitioners who seek to understand the latest developments will profit from this handbook.

Time Series Analysis for the Social Sciences

Time Series Analysis for the Social Sciences
Author: Janet M. Box-Steffensmeier
Publisher: Cambridge University Press
Total Pages: 297
Release: 2014-12-22
Genre: Political Science
ISBN: 1316060500

Time series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time series properties of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. Janet M. Box-Steffensmeier, John R. Freeman, Jon C. Pevehouse and Matthew P. Hitt cover a wide range of topics including ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting. This book is aimed at researchers and graduate students who have taken at least one course in multivariate regression. Examples are drawn from several areas of social science, including political behavior, elections, international conflict, criminology, and comparative political economy.