Recent Advances In Time Series Forecasting
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Recent Advances In Time Series Forecasting
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Author : Dinesh C.S. Bisht
language : en
Publisher: CRC Press
Release Date : 2021-09-08
Recent Advances In Time Series Forecasting written by Dinesh C.S. Bisht and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-08 with Mathematics categories.
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.
Recent Advances In Time Series Forecasting
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Author : Dinesh C.S. Bisht
language : en
Publisher: CRC Press
Release Date : 2021-09-07
Recent Advances In Time Series Forecasting written by Dinesh C.S. Bisht and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-07 with Mathematics categories.
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.
Recent Advances In Time Series Classification Methodology And Applications
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Author : Zoltán Gellér
language : en
Publisher: Springer Nature
Release Date : 2025-04-26
Recent Advances In Time Series Classification Methodology And Applications written by Zoltán Gellér and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-26 with Technology & Engineering categories.
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.
Time Series Analysis Recent Advances New Perspectives And Applications
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Author : Jorge Rocha
language : en
Publisher: BoD – Books on Demand
Release Date : 2024-05-22
Time Series Analysis Recent Advances New Perspectives And Applications written by Jorge Rocha and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-22 with Mathematics categories.
Time series analysis describes, explains, and predicts changes in a phenomenon through time. People have utilized techniques that add a distinctive spatial dimension to this type of analysis. Major applications of spatiotemporal analysis include forecasting epidemics, analyzing the development of traffic conditions in urban networks, and forecasting/backcasting economic risks such as those associated with changing house prices and the occurrence of hazardous events. This book includes contributions from researchers, scholars, and professionals about the most recent theory, models, and applications for interdisciplinary and multidisciplinary research encircling disciplines of computer science, mathematics, statistics, geography, and more in time series analysis and forecasting/backcasting.
Theory And Applications Of Time Series Analysis
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Author : Olga Valenzuela
language : en
Publisher: Springer Nature
Release Date : 2020-11-20
Theory And Applications Of Time Series Analysis written by Olga Valenzuela and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-20 with Business & Economics categories.
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.
Computational Intelligence In Time Series Forecasting
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Author : Ajoy K. Palit
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-07-01
Computational Intelligence In Time Series Forecasting written by Ajoy K. Palit and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-07-01 with Computers categories.
Foresight in an engineering business can make the difference between success and failure, and can be vital to the effective control of industrial systems. The authors of this book harness the power of intelligent technologies individually and in combination.
Advanced Time Series Data Analysis
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Author : I. Gusti Ngurah Agung
language : en
Publisher: John Wiley & Sons
Release Date : 2018-12-28
Advanced Time Series Data Analysis written by I. Gusti Ngurah Agung and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-28 with Mathematics categories.
Introduces the latest developments in forecasting in advanced quantitative data analysis This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable. Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers. Presents models that are all classroom tested Contains real-life data samples Contains over 350 equation specifications of various time series models Contains over 200 illustrative examples with special notes and comments Applicable for time series data of all quantitative studies Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.
Time Series Forecasting Using Deep Learning
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Author : Ivan Gridin
language : en
Publisher: BPB Publications
Release Date : 2021-10-15
Time Series Forecasting Using Deep Learning written by Ivan Gridin and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-15 with Computers categories.
Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks KEY FEATURES ● Covers numerous concepts, techniques, best practices and troubleshooting tips by community experts. ● Includes practical demonstration of robust deep learning prediction models with exciting use-cases. ● Covers the use of the most powerful research toolkit such as Python, PyTorch, and Neural Network Intelligence. DESCRIPTION This book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch. The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task. Finally by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques. WHAT YOU WILL LEARN ● Work with the Encoder-Decoder concept and Temporal Convolutional Network mechanics. ● Learn the basics of neural architecture search with Neural Network Intelligence. ● Combine standard statistical analysis methods with deep learning approaches. ● Automate the search for optimal predictive architecture. ● Design your custom neural network architecture for specific tasks. ● Apply predictive models to real-world problems of forecasting stock quotes, weather, and natural processes. WHO THIS BOOK IS FOR This book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed. TABLE OF CONTENTS 1. Time Series Problems and Challenges 2. Deep Learning with PyTorch 3. Time Series as Deep Learning Problem 4. Recurrent Neural Networks 5. Advanced Forecasting Models 6. PyTorch Model Tuning with Neural Network Intelligence 7. Applying Deep Learning to Real-world Forecasting Problems 8. PyTorch Forecasting Package 9. What is Next?
Neural Network Time Series
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Author : E. Michael Azoff
language : en
Publisher:
Release Date : 1994-09-27
Neural Network Time Series written by E. Michael Azoff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-09-27 with Business & Economics categories.
Comprehensively specified benchmarks are provided (including weight values), drawn from time series examples in chaos theory and financial futures. The book covers data preprocessing, random walk theory, trading systems and risk analysis. It also provides a literature review, a tutorial on backpropagation, and a chapter on further reading and software.
Proceedings Of The International Conference On Recent Advances In Artificial Intelligence For Sustainable Development Raisd 2025
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Author : Piyush Ranjan
language : en
Publisher: Springer Nature
Release Date : 2025-08-18
Proceedings Of The International Conference On Recent Advances In Artificial Intelligence For Sustainable Development Raisd 2025 written by Piyush Ranjan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-18 with Computers categories.
This open access volume presents select proceedings of Recent Advances in Artificial Intelligence for Sustainable Development (RAISD).