Exploratory Causal Analysis With Time Series Data
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Exploratory Causal Analysis with Time Series Data
Author: James M. McCracken
language: en
Publisher: Springer Nature
Release Date: 2022-06-01
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.
Kaggle Kernels in Action
Unlock the power of data science and machine learning with "Kaggle Kernels in Action: From Exploration to Competition." This comprehensive guide offers a structured approach for both beginners and seasoned data enthusiasts, transforming complex concepts into accessible knowledge. Dive deep into the world of Kaggle, the premier platform that bridges learning and application, equipping you with the skills necessary to excel in the dynamic field of data science. Each chapter meticulously addresses critical aspects of the Kaggle experience—from setting up an efficient working environment and mastering data exploration techniques to constructing robust models and tackling real-world challenges. Learn from detailed analyses and case studies that showcase the impact Kaggle has on industries across the globe. This book offers you a roadmap to developing strategies for effective competition engagement and collaboration, ensuring your efforts translate into tangible outcomes. Experience the transformative journey of data science mastery with this indispensable resource. Embrace a learning process enriched by best practices, community engagement, and actionable insights, to hone your analytical prowess and expand your professional horizons. "Kaggle Kernels in Action" not only prepares you for success on Kaggle but empowers you for an enduring career in the evolving landscape of machine learning and data analytics.
Exploiting the Power of Group Differences
This book presents pattern-based problem-solving methods for a variety of machine learning and data analysis problems. The methods are all based on techniques that exploit the power of group differences. They make use of group differences represented using emerging patterns (aka contrast patterns), which are patterns that match significantly different numbers of instances in different data groups. A large number of applications outside of the computing discipline are also included. Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as simple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor interactions. The length of EPs can be used to detect anomalies, outliers, and novelties. Emerging/contrast pattern based methods for clustering analysis and outlier detection do not need distance metrics, avoiding pitfalls of the latter in exploratory analysis of high dimensional data. EP-based classifiers can achieve good accuracy even when the training datasets are tiny, making them useful for exploratory compound selection in drug design. EPs can serve as opportunities in opportunity-focused boosting and are useful for constructing powerful conditional ensembles. EP-based methods often produce interpretable models and results. In general, EPs are useful for classification, clustering, outlier detection, gene ranking for complex diseases, prediction model analysis and improvement, and so on. EPs are useful for many tasks because they represent group differences, which have extraordinary power. Moreover, EPs represent multi-factor interactions, whose effective handling is of vital importance and is a major challenge in many disciplines. Based on the results presented in this book, one can clearly say that patterns are useful, especially when they are linked to issues of interest. We believe that many effective ways to exploit group differences' power still remain to be discovered. Hopefully this book will inspire readers to discover such new ways, besides showing them existing ways, to solve various challenging problems.