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Data Driven Methods For Time Series Forecasting Classification And Uncertainty Quantification
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Book Synopsis Data-driven Methods for Time Series Forecasting, Classification, and Uncertainty Quantification by : Diya Sashidhar
Download or read book Data-driven Methods for Time Series Forecasting, Classification, and Uncertainty Quantification written by Diya Sashidhar and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increased availability of time series data has led to a burgeoning interest in data-driven modeling and time series analysis. The ability to model temporal data can not only enable us to discover patterns inherent in a set of collected measurements, but also predict future trends. However, collected temporal measurements are oftentimes artifacted with noise, making it difficult to discern the actual signal. This presence of noise can greatly bias models, resulting in inaccurate forecasts with high uncertainty. In this thesis,I demonstrate how data-driven methods can be applied to a wide array of artifacted data while circumventing noise-induced bias.I first show the application of various data-driven methods and signal processing techniques on labeled time series data. Specifically, I apply supervised machine learning and signal processing techniques on corrupted electrocardiograms (ECGs) in order to classify pulse status in patients undergoing cardiac arrest. I then introduce a data-driven method that leverages statistical bagging and optimized Dynamic Mode Decomposition (optDMD) in order to produce accurate long-term forecasting and spatial and temporal uncertainty quantification for unlabeled, non-stationary time series. I then highlight the robustness of this method by applying it to corrupted flu transmission data in order to predict future flu trends as well as gain insight into temporal cycles of modes.
Book Synopsis Time Series Analysis by : Chun-Kit Ngan
Download or read book Time Series Analysis written by Chun-Kit Ngan and published by BoD – Books on Demand. This book was released on 2019-11-06 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to provide readers with the current information, developments, and trends in a time series analysis, particularly in time series data patterns, technical methodologies, and real-world applications. This book is divided into three sections and each section includes two chapters. Section 1 discusses analyzing multivariate and fuzzy time series. Section 2 focuses on developing deep neural networks for time series forecasting and classification. Section 3 describes solving real-world domain-specific problems using time series techniques. The concepts and techniques contained in this book cover topics in time series research that will be of interest to students, researchers, practitioners, and professors in time series forecasting and classification, data analytics, machine learning, deep learning, and artificial intelligence.
Book Synopsis A Data Driven Based Comparison Study of Statistical and Deep Learning Based Time Series Forecasting Methods for Infectious Disease Modeling and Financial Data by : Vinay Kumar Reddy Chimmula
Download or read book A Data Driven Based Comparison Study of Statistical and Deep Learning Based Time Series Forecasting Methods for Infectious Disease Modeling and Financial Data written by Vinay Kumar Reddy Chimmula and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In regard to human history, it isn't that long since forecasting transitioned from the spiritual and mythical components into the scientific domain. In recent years, forecasting has become an integral part of the mathematical analysis. It has a wide range of significance in various domains and is critical in some life-saving applications. The crucial element in determining the suitable forecasting model is its accuracy. Most of the existing approaches are either based on the statistical or random analysis. One of the limitations of such models is the failure to capture the nonlinearities that are present in the data. Given the inadequacy of classical models in processing hidden non-linear sequences, deep learning models have been showing better results in time series forecasting applications. We addressed this issue by proposing a deep learning-based LSTM model to solve various time series problems. In order to justify our claims, the proposed LSTM models are tested on various datasets including retail, financial and epidemiological data. Forecasting results of different models show that statistical models outperformed deep learning models on small datasets. Meanwhile, deep learning models performed well on large nonstationary data sets. Deep learning-based time series forecasting models are being used in largescale real-world applications over the last few years. After winning the recent M4 competition, the popularity of Deep Learning based models is not only confirmed to academia but also being used for industrial applications. In addition to that, in this novel research, we modeled the current COVID-19 pandemic using deep learning-based time series modeling. This thesis aims at time series modeling and forecasting under different circumstances using statistical and deep learning approaches for various unexplored applications. We addressed the limitations of traditional time series forecasting procedures and proposed various deep learning architectures with multi-layer Recurrent Networks (RNN) and how they may be exploited for time series forecasting problems. The new abilities of neural networks in generating complex mapping functions, feature extraction tools and support for sequential are provided by Recurrent Neural Networks (RNN) and Long Short-Term Memory networks (LSTM). Finally, we outlined the underlying factors behind the success of Deep Learning (DL) methods and given some directions for future applications. keywords: Time Series Forecasting, Infectious.
Book Synopsis Bayesian Reinforcement Learning by : Mohammad Ghavamzadeh
Download or read book Bayesian Reinforcement Learning written by Mohammad Ghavamzadeh and published by . This book was released on 2015-11-18 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.
Book Synopsis Time Series Clustering and Classification by : Elizabeth Ann Maharaj
Download or read book Time Series Clustering and Classification written by Elizabeth Ann Maharaj and published by CRC Press. This book was released on 2019-03-19 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website
Book Synopsis AI-Driven Time Series Forecasting by : Raghurami Reddy Etukuru Ph.D.
Download or read book AI-Driven Time Series Forecasting written by Raghurami Reddy Etukuru Ph.D. and published by iUniverse. This book was released on 2023-10-06 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: When you enter the world of time series analysis, you step into a labyrinth of numerical patterns, where each turn you take unveils another layer of complexity. Here, simple mathematical or statistical models struggle to keep pace. Reality is riddled with complex patterns in time series data, which, like cryptic pieces of a jigsaw puzzle, hold the key to unraveling insightful predictions. These complex patterns include non-linearity, non-stationarity, long memory or dependence, asymmetry, and stochasticity. But what creates these intricate patterns? Raghurami Reddy Etukuru, Ph.D., a distinguished and adaptable specialist in data science and artificial intelligence, delves into that question in this groundbreaking book, explaining that the factors are numerous and multifaceted, each adding their own measure of challenge. He doesn't just discuss problems but also addresses the forecasting of time series amidst intricate patterns. Take a deep dive deep into the world of numbers and patterns, so you can unravel complexities and leverage the power of artificial intelligence to enhance predictive capabilities. More than just a theoretical guide, this book is a practical companion in the often-turbulent journey of understanding and predicting complex time series data.
Book Synopsis Practical Time Series Analysis by : Aileen Nielsen
Download or read book Practical Time Series Analysis written by Aileen Nielsen and published by O'Reilly Media. This book was released on 2019-09-20 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Computational Approaches for Time Series Analysis and Prediction by : Yang Lan
Download or read book Computational Approaches for Time Series Analysis and Prediction written by Yang Lan and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Time series data mining is one branch of data mining. Time series analysis and prediction have always played an important role in human activities and natural sciences. A Pseudo-Periodical time series has a complex structure, with fluctuations and frequencies of the times series changing over time. Currently, Pseudo-Periodicity of time series brings new properties and challenges to time series analysis and prediction. This thesis proposes two original computational approaches for time series analysis and prediction: Moving Average of nth-order Difference (MANoD) and Series Features Extraction (SFE). Based on data-driven methods, the two original approaches open new insights in time series analysis and prediction contributing with new feature detection techniques. The proposed algorithms can reveal hidden patterns based on the characteristics of time series, and they can be applied for predicting forthcoming events. This thesis also presents the evaluation results of proposed algorithms on various pseudo-periodical time series, and compares the predicting results with classical time series prediction methods. The results of the original approaches applied to real world and synthetic time series are very good and show that the contributions open promising research directions.
Book Synopsis Codeless Time Series Analysis with KNIME by : Corey Weisinger
Download or read book Codeless Time Series Analysis with KNIME written by Corey Weisinger and published by Packt Publishing Ltd. This book was released on 2022-08-19 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Perform time series analysis using KNIME Analytics Platform, covering both statistical methods and machine learning-based methods Key Features • Gain a solid understanding of time series analysis and its applications using KNIME • Learn how to apply popular statistical and machine learning time series analysis techniques • Integrate other tools such as Spark, H2O, and Keras with KNIME within the same application Book Description This book will take you on a practical journey, teaching you how to implement solutions for many use cases involving time series analysis techniques. This learning journey is organized in a crescendo of difficulty, starting from the easiest yet effective techniques applied to weather forecasting, then introducing ARIMA and its variations, moving on to machine learning for audio signal classification, training deep learning architectures to predict glucose levels and electrical energy demand, and ending with an approach to anomaly detection in IoT. There's no time series analysis book without a solution for stock price predictions and you'll find this use case at the end of the book, together with a few more demand prediction use cases that rely on the integration of KNIME Analytics Platform and other external tools. By the end of this time series book, you'll have learned about popular time series analysis techniques and algorithms, KNIME Analytics Platform, its time series extension, and how to apply both to common use cases. What you will learn • Install and configure KNIME time series integration • Implement common preprocessing techniques before analyzing data • Visualize and display time series data in the form of plots and graphs • Separate time series data into trends, seasonality, and residuals • Train and deploy FFNN and LSTM to perform predictive analysis • Use multivariate analysis by enabling GPU training for neural networks • Train and deploy an ML-based forecasting model using Spark and H2O Who this book is for This book is for data analysts and data scientists who want to develop forecasting applications on time series data. While no coding skills are required thanks to the codeless implementation of the examples, basic knowledge of KNIME Analytics Platform is assumed. The first part of the book targets beginners in time series analysis, and the subsequent parts of the book challenge both beginners as well as advanced users by introducing real-world time series applications.
Book Synopsis Advances in Time Series Forecasting by : Cagdas Hakan Aladag
Download or read book Advances in Time Series Forecasting written by Cagdas Hakan Aladag and published by Bentham Science Publishers. This book was released on 2012 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Time series analysis is applicable in a variety of disciplines such as business administration, economics, public finances, engineering, statistics, econometrics, mathematics and actuarial sciences. Forecasting the future assists in critical organizationa"
Book Synopsis Advances in Time Series Analysis and Forecasting by : Ignacio Rojas
Download or read book Advances in Time Series Analysis and Forecasting written by Ignacio Rojas and published by Springer. This book was released on 2017-07-31 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.
Book Synopsis Advanced Analytics and Learning on Temporal Data by : Vincent Lemaire
Download or read book Advanced Analytics and Learning on Temporal Data written by Vincent Lemaire and published by Springer Nature. This book was released on 2021-12-02 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection.
Book Synopsis The Proceedings of the 18th Annual Conference of China Electrotechnical Society by : Qingxin Yang
Download or read book The Proceedings of the 18th Annual Conference of China Electrotechnical Society written by Qingxin Yang and published by Springer Nature. This book was released on with total page 876 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data-driven Methods for Simulation and Forecasting of Financial Time Series by : Chao Zhang
Download or read book Data-driven Methods for Simulation and Forecasting of Financial Time Series written by Chao Zhang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note is part of Quality testing.
Book Synopsis Time Series for Data Science by : Wayne A. Woodward
Download or read book Time Series for Data Science written by Wayne A. Woodward and published by CRC Press. This book was released on 2022-08-01 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science students and practitioners want to find a forecast that “works” and don’t want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensemble modelling for combining information from several strategies. Covering time series regression models, exponential smoothing, Holt-Winters forecasting, and Neural Networks. It places a particular emphasis on classical ARMA and ARIMA models that is often lacking from other textbooks on the subject. This book is an accessible guide that doesn’t require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed. Features: Provides a thorough coverage and comparison of a wide array of time series models and methods: Exponential Smoothing, Holt Winters, ARMA and ARIMA, deep learning models including RNNs, LSTMs, GRUs, and ensemble models composed of combinations of these models. Introduces the factor table representation of ARMA and ARIMA models. This representation is not available in any other book at this level and is extremely useful in both practice and pedagogy. Uses real world examples that can be readily found via web links from sources such as the US Bureau of Statistics, Department of Transportation and the World Bank. There is an accompanying R package that is easy to use and requires little or no previous R experience. The package implements the wide variety of models and methods presented in the book and has tremendous pedagogical use.
Book Synopsis Recent Advances in Time Series Forecasting by : Dinesh C.S. Bisht
Download or read book Recent Advances in Time Series Forecasting written by Dinesh C.S. Bisht and published by CRC Press. This book was released on 2021-09-08 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Future predictions are always a topic of interest. Precise estimates are crucial in many activities as forecasting errors can lead to big financial loss. The sequential analysis of data and information gathered from past to present is call time series analysis. This book covers the recent advancements in time series forecasting. The book includes theoretical as well as recent applications of time series analysis. It focuses on the recent techniques used, discusses a combination of methodology and applications, presents traditional and advanced tools, new applications, and identifies the gaps in knowledge in engineering applications. This book is aimed at scientists, researchers, postgraduate students and engineers in the areas of supply chain management, production, inventory planning, and statistical quality control.
Book Synopsis Change Detection and Image Time Series Analysis 2 by : Abdourrahmane M. Atto
Download or read book Change Detection and Image Time Series Analysis 2 written by Abdourrahmane M. Atto and published by John Wiley & Sons. This book was released on 2021-12-29 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches. Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations, Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.
