Recent Advances In Time Series Classification Methodology And Applications
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Recent Advances in Time-Series Classification—Methodology and Applications
This book examines the impact of such constraints on elastic time-series similarity measures and provides guidance on selecting suitable measures. Time-series classification frequently relies on selecting an appropriate similarity or distance measure to compare time series effectively, often using dynamic programming techniques for more robust results. However, these techniques can be computationally demanding, which results in the usage of global constraints to reduce the search area in the dynamic programming matrix. While these constraints cut computation time significantly (by up to three orders of magnitude), they may also affect classification accuracy. Additionally, the importance of the nearest neighbor classifier (1NN) is emphasized for its strong performance in time-series classification, alongside the kNN classifier which offers stable results. This book further explores the weighted kNN classifier, which gives closer neighbors more influence, showing how it merges accuracy and stability for improved classification outcomes.
Theory and Applications of Time Series Analysis
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.
Recent Advances in Information and Communication Technology 2019
This book presents the latest research on computer recognition systems. Over the last few years, computer scientists, engineers and users have been confronted with rapid changes in computer interfaces and in the abilities of the machines and the services available. And this is just the beginning: based on recent research findings, we can expect more significant advances and challenges in the next decade. Achievements in the area of artificial intelligence have made an important major contribution to these developments: Machine learning, natural language processing, speech recognition, image and video processing are just some of the major research and engineering directions that have made autonomous driving, language assistants, automatic translation and answering systems as well as other innovative applications such as more human-oriented interfaces possible. Those developments also reflect economic changes in the world, which are increasingly dominated by the needs of enhanced globalization, international cooperation (including its competitive aspects) and emerging global problems.