Recent Advances In Time Series Forecasting

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Recent Advances in Time Series Forecasting

Dinesh C.S. Bisht,Mangey Ram
Recent Advances in Time Series Forecasting Book PDF

Author : Dinesh C.S. Bisht,Mangey Ram
Publisher : CRC Press
Page : 183 pages
File Size : 53,5 Mb
Release : 2021-09-08
Category : Mathematics
ISBN : 9781000433845

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Recent Advances in Time Series Forecasting by Dinesh C.S. Bisht,Mangey Ram Pdf

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.

Advances in Time Series Forecasting

Cagdas Hakan Aladag,Erol Eǧrioǧlu
Advances in Time Series Forecasting Book PDF

Author : Cagdas Hakan Aladag,Erol Eǧrioǧlu
Publisher : Bentham Science Publishers
Page : 143 pages
File Size : 50,7 Mb
Release : 2012
Category : Mathematics
ISBN : 9781608053735

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Advances in Time Series Forecasting by Cagdas Hakan Aladag,Erol Eǧrioǧlu Pdf

"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"

Advances in Time Series Forecasting

Cagdas Hakan Aladag
Advances in Time Series Forecasting Book PDF

Author : Cagdas Hakan Aladag
Publisher : Bentham Science Publishers
Page : 196 pages
File Size : 54,7 Mb
Release : 2017-12-06
Category : Mathematics
ISBN : 9781681085289

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Advances in Time Series Forecasting by Cagdas Hakan Aladag Pdf

This volume is a valuable source of recent knowledge about advanced time series forecasting techniques such as artificial neural networks, fuzzy time series, or hybrid approaches. New forecasting frameworks are discussed and their application is demonstrated. The second volume of the series includes applications of some powerful forecasting approaches with a focus on fuzzy time series methods. Chapters integrate these methods with concepts such as neural networks, high order multivariate systems, deterministic trends, distance measurement and much more. The chapters are contributed by eminent scholars and serve to motivate and accelerate future progress while introducing new branches of time series forecasting. This book is a valuable resource for MSc and PhD students, academic personnel and researchers seeking updated and critically important information on the concepts of advanced time series forecasting and its applications.

Advances in Time Series Analysis and Forecasting

Ignacio Rojas,Héctor Pomares,Olga Valenzuela
Advances in Time Series Analysis and Forecasting Book PDF

Author : Ignacio Rojas,Héctor Pomares,Olga Valenzuela
Publisher : Springer
Page : 414 pages
File Size : 45,5 Mb
Release : 2017-07-31
Category : Business & Economics
ISBN : 9783319557892

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Advances in Time Series Analysis and Forecasting by Ignacio Rojas,Héctor Pomares,Olga Valenzuela Pdf

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.

Modern Time Series Forecasting with Python

Manu Joseph
Modern Time Series Forecasting with Python Book PDF

Author : Manu Joseph
Publisher : Packt Publishing Ltd
Page : 552 pages
File Size : 40,8 Mb
Release : 2022-11-24
Category : Computers
ISBN : 9781803232041

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Modern Time Series Forecasting with Python by Manu Joseph Pdf

Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts Key Features Explore industry-tested machine learning techniques used to forecast millions of time series Get started with the revolutionary paradigm of global forecasting models Get to grips with new concepts by applying them to real-world datasets of energy forecasting Book DescriptionWe live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.What you will learn Find out how to manipulate and visualize time series data like a pro Set strong baselines with popular models such as ARIMA Discover how time series forecasting can be cast as regression Engineer features for machine learning models for forecasting Explore the exciting world of ensembling and stacking models Get to grips with the global forecasting paradigm Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer Explore multi-step forecasting and cross-validation strategies Who this book is for The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.

Forecasting: principles and practice

Rob J Hyndman,George Athanasopoulos
Forecasting principles and practice Book PDF

Author : Rob J Hyndman,George Athanasopoulos
Publisher : OTexts
Page : 380 pages
File Size : 53,8 Mb
Release : 2018-05-08
Category : Business & Economics
ISBN : 9780987507112

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Forecasting: principles and practice by Rob J Hyndman,George Athanasopoulos Pdf

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

Theory and Applications of Time Series Analysis

Olga Valenzuela,Fernando Rojas,Luis Javier Herrera,Héctor Pomares,Ignacio Rojas
Theory and Applications of Time Series Analysis Book PDF

Author : Olga Valenzuela,Fernando Rojas,Luis Javier Herrera,Héctor Pomares,Ignacio Rojas
Publisher : Springer Nature
Page : 460 pages
File Size : 44,7 Mb
Release : 2020-11-20
Category : Business & Economics
ISBN : 9783030562199

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Theory and Applications of Time Series Analysis by Olga Valenzuela,Fernando Rojas,Luis Javier Herrera,Héctor Pomares,Ignacio Rojas Pdf

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-based Time Series Analysis

Dinesh C. S. Bisht,Mangey Ram
Computational Intelligence based Time Series Analysis Book PDF

Author : Dinesh C. S. Bisht,Mangey Ram
Publisher : CRC Press
Page : 191 pages
File Size : 52,9 Mb
Release : 2022-11-30
Category : Science
ISBN : 9781000793819

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Computational Intelligence-based Time Series Analysis by Dinesh C. S. Bisht,Mangey Ram Pdf

The sequential analysis of data and information gathered from past to present is called time series analysis. Time series data are of high dimension, large size and updated continuously. A time series depends on various factors like trend, seasonality, cycle and irregular data set, and is basically a series of data points well-organized in time. Time series forecasting is a significant area of machine learning. There are various prediction problems that are time-dependent and these problems can be handled through time series analysis. Computational intelligence (CI) is a developing computing approach for the forthcoming several years. CI gives the litheness to model the problem according to given requirements. It helps to find swift solutions to the problems arising in numerous disciplines. These methods mimic human behavior. The main objective of CI is to develop intelligent machines to provide solutions to real world problems, which are not modelled or are too difficult to model mathematically. This book aims to cover the recent advances in time series and applications of CI for time series analysis.

Time Series Analysis and Forecasting

Ignacio Rojas,Héctor Pomares,Olga Valenzuela
Time Series Analysis and Forecasting Book PDF

Author : Ignacio Rojas,Héctor Pomares,Olga Valenzuela
Publisher : Springer
Page : 340 pages
File Size : 45,9 Mb
Release : 2018-10-03
Category : Business & Economics
ISBN : 9783319969442

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Time Series Analysis and Forecasting by Ignacio Rojas,Héctor Pomares,Olga Valenzuela Pdf

This book presents selected peer-reviewed contributions from the International Work-Conference on Time Series, ITISE 2017, held in Granada, Spain, September 18-20, 2017. It discusses topics in time series analysis and forecasting, including advanced mathematical methodology, computational intelligence methods for time series, dimensionality reduction and similarity measures, econometric models, energy time series forecasting, forecasting in real problems, online learning in time series as well as high-dimensional and complex/big data time series. 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 computer science, mathematics, statistics and econometrics.

The Analysis of Time Series

Chris Chatfield
The Analysis of Time Series Book PDF

Author : Chris Chatfield
Publisher : CRC Press
Page : 349 pages
File Size : 47,6 Mb
Release : 2016-03-30
Category : Mathematics
ISBN : 9780203491683

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The Analysis of Time Series by Chris Chatfield Pdf

Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. With each successive edition, bestselling author Chris Chatfield has honed and refined his presentation, updated the material to reflect advances in the field, and presented interesting new data sets. The sixth edition is no exception. It provides an accessible, comprehensive introduction to the theory and practice of time series analysis. The treatment covers a wide range of topics, including ARIMA probability models, forecasting methods, spectral analysis, linear systems, state-space models, and the Kalman filter. It also addresses nonlinear, multivariate, and long-memory models. The author has carefully updated each chapter, added new discussions, incorporated new datasets, and made those datasets available for download from www.crcpress.com. A free online appendix on time series analysis using R can be accessed at http://people.bath.ac.uk/mascc/TSA.usingR.doc. Highlights of the Sixth Edition: A new section on handling real data New discussion on prediction intervals A completely revised and restructured chapter on more advanced topics, with new material on the aggregation of time series, analyzing time series in finance, and discrete-valued time series A new chapter of examples and practical advice Thorough updates and revisions throughout the text that reflect recent developments and dramatic changes in computing practices over the last few years The analysis of time series can be a difficult topic, but as this book has demonstrated for two-and-a-half decades, it does not have to be daunting. The accessibility, polished presentation, and broad coverage of The Analysis of Time Series make it simply the best introduction to the subject available.

Theory and Applications of Time Series Analysis and Forecasting

Olga Valenzuela,Fernando Rojas,Luis Javier Herrera,Héctor Pomares,Ignacio Rojas
Theory and Applications of Time Series Analysis and Forecasting Book PDF

Author : Olga Valenzuela,Fernando Rojas,Luis Javier Herrera,Héctor Pomares,Ignacio Rojas
Publisher : Springer Nature
Page : 331 pages
File Size : 41,5 Mb
Release : 2023-04-04
Category : Mathematics
ISBN : 9783031141973

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Theory and Applications of Time Series Analysis and Forecasting by Olga Valenzuela,Fernando Rojas,Luis Javier Herrera,Héctor Pomares,Ignacio Rojas Pdf

This book presents a selection of peer-reviewed contributions on the latest developments in time series analysis and forecasting, presented at the 7th International Conference on Time Series and Forecasting, ITISE 2021, held in Gran Canaria, Spain, July 19-21, 2021. It is divided into four parts. The first part addresses general modern methods and theoretical aspects of time series analysis and forecasting, while the remaining three parts focus on forecasting methods in econometrics, time series forecasting and prediction, and numerous other real-world applications. Covering a broad range of topics, the book will give readers a modern perspective on the subject. 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.

Machine Learning for Time Series Forecasting with Python

Francesca Lazzeri
Machine Learning for Time Series Forecasting with Python Book PDF

Author : Francesca Lazzeri
Publisher : John Wiley & Sons
Page : 224 pages
File Size : 45,5 Mb
Release : 2020-12-03
Category : Computers
ISBN : 9781119682387

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Machine Learning for Time Series Forecasting with Python by Francesca Lazzeri Pdf

Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Advanced Time Series Data Analysis

I. Gusti Ngurah Agung
Advanced Time Series Data Analysis Book PDF

Author : I. Gusti Ngurah Agung
Publisher : John Wiley & Sons
Page : 538 pages
File Size : 46,9 Mb
Release : 2019-03-18
Category : Mathematics
ISBN : 9781119504719

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Advanced Time Series Data Analysis by I. Gusti Ngurah Agung Pdf

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.

Forecasting Time Series Data with Facebook Prophet

Greg Rafferty
Forecasting Time Series Data with Facebook Prophet Book PDF

Author : Greg Rafferty
Publisher : Packt Publishing Ltd
Page : 270 pages
File Size : 45,8 Mb
Release : 2021-03-12
Category : Computers
ISBN : 9781800566521

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Forecasting Time Series Data with Facebook Prophet by Greg Rafferty Pdf

Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using Python Key Features Learn how to use the open-source forecasting tool Facebook Prophet to improve your forecasts Build a forecast and run diagnostics to understand forecast quality Fine-tune models to achieve high performance, and report that performance with concrete statistics Book Description Prophet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your first model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments. By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code. What you will learn Gain an understanding of time series forecasting, including its history, development, and uses Understand how to install Prophet and its dependencies Build practical forecasting models from real datasets using Python Understand the Fourier series and learn how it models seasonality Decide when to use additive and when to use multiplicative seasonality Discover how to identify and deal with outliers in time series data Run diagnostics to evaluate and compare the performance of your models Who this book is for This book is for data scientists, data analysts, machine learning engineers, software engineers, project managers, and business managers who want to build time series forecasts in Python. Working knowledge of Python and a basic understanding of forecasting principles and practices will be useful to apply the concepts covered in this book more easily.

Time Series Analysis

Chun-Kit Ngan
Time Series Analysis Book PDF

Author : Chun-Kit Ngan
Publisher : BoD – Books on Demand
Page : 131 pages
File Size : 45,7 Mb
Release : 2019-11-06
Category : Mathematics
ISBN : 9781789847789

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Time Series Analysis by Chun-Kit Ngan Pdf

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.