Artificial Intelligence In Forecasting
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Artificial Intelligence In Forecasting
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Author : Sachi Mohanty
language : en
Publisher: CRC Press
Release Date : 2024-07-19
Artificial Intelligence In Forecasting written by Sachi Mohanty and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-19 with Computers categories.
Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data. The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.
Forecasting With Artificial Intelligence
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Author : Mohsen Hamoudia
language : en
Publisher: Springer Nature
Release Date : 2023-09-20
Forecasting With Artificial Intelligence written by Mohsen Hamoudia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-20 with Business & Economics categories.
This book is a comprehensive guide that explores the intersection of artificial intelligence and forecasting, providing the latest insights and trends in this rapidly evolving field. The book contains fourteen chapters covering a wide range of topics, including the concept of AI, its impact on economic decision-making, traditional and machine learning-based forecasting methods, challenges in demand forecasting, global forecasting models, meta-learning and feature-based forecasting, ensembling, deep learning, scalability in industrial and optimization applications, and forecasting performance evaluation. With key illustrations, state-of-the-art implementations, best practices, and notable advances, this book offers practical insights into the theory and practice of AI-based forecasting. This book is a valuable resource for anyone involved in forecasting, including forecasters, statisticians, data scientists, business analysts, or decision-makers.
Business Forecasting
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Author : Michael Gilliland
language : en
Publisher: John Wiley & Sons
Release Date : 2021-04-29
Business Forecasting written by Michael Gilliland 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 2021-04-29 with Business & Economics categories.
Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.
Business Forecasting
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Author : Michael Gilliland
language : en
Publisher: John Wiley & Sons
Release Date : 2021-04-28
Business Forecasting written by Michael Gilliland 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 2021-04-28 with Business & Economics categories.
Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.
Application Of Artificial Intelligence In Forecasting
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Author : Albert Annor Antwi
language : en
Publisher:
Release Date : 2019
Application Of Artificial Intelligence In Forecasting written by Albert Annor Antwi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Purpose:- The aim of this reach is to identify how Artificial Intelligence (AI) could be used in enhancing forecasting to achieve more accurate outcomes. The research also explores the influence that forecasting has on global economy and the reasons why it needs to be accurate. Also, the research explains various pitfalls identified in forecasting. Method:- This research implements two research approaches which are review of literature and formulation of hypotheses. Seven hypotheses are created. Findings:- AI, when integrated with other technologies such as Machine Learning (ML) and when provided with the right computer power, yields much more accurate results than many other forecasting methods. The technology is costly, however, and it is prone to cyber-attacks. Conclusion:- The future of business is highly reliant on forecasting, which directly impacts the global economy. But, not every business will have the power to own the forecasting technology due to the cost, and business will need to increase security to protect the forecasting systems.
Artificial Intelligence In Forecasting
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Author : Sachi Mohanty
language : en
Publisher: CRC Press
Release Date : 2024-07-19
Artificial Intelligence In Forecasting written by Sachi Mohanty and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-19 with Computers categories.
Forecasting deals with the uncertainty of the future. To be effective, forecasting models should be timely available, accurate, reliable, and compatible with existing database. Accurate projection of the future is of vital importance in supply chain management, inventory control, economic condition, technology, growth trend, social change, political change, business, weather forecasting, stock price prediction, earthquake prediction, etc. AI powered tools and techniques of forecasting play a major role in improving the projection accuracy. The software running AI forecasting models use machine learning to improve accuracy. The software can analyse the past data and can make better prediction about the future trends with higher accuracy and confidence that favours for making proper future planning and decision. In other words, accurate forecasting requires more than just the matching of models to historical data. The book covers the latest techniques used by managers in business today, discover the importance of forecasting and learn how it's accomplished. Readers will also be familiarised with the necessary skills to meet the increased demand for thoughtful and realistic forecasts.
Time Series Forecasting Using Generative Ai
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Author : Banglore Vijay Kumar Vishwas
language : en
Publisher: Springer Nature
Release Date : 2025-03-24
Time Series Forecasting Using Generative Ai written by Banglore Vijay Kumar Vishwas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-24 with Mathematics categories.
"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies." The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs. This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights. !-- [if !supportLists]--● !--[endif]--Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting. !-- [if !supportLists]--● !--[endif]--Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting. !-- [if !supportLists]--● !--[endif]--Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting. !-- [if !supportLists]--● !--[endif]--Explore complex foundation models like Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM. !-- [if !supportLists]--● !--[endif]--Gain practical knowledge on how to apply Gen AI techniques to real-world time series forecasting challenges and make data-driven decisions. Who this book is for: Data Scientists, Machine learning engineers, Business Aanalysts, Statisticians, Economists, Financial Analysts, Operations Research Analysts, Data Analysts, Students.
Artificial Intelligence For Air Quality Monitoring And Prediction
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Author : Amit Awasthi
language : en
Publisher: CRC Press
Release Date : 2024-10-02
Artificial Intelligence For Air Quality Monitoring And Prediction written by Amit Awasthi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Technology & Engineering categories.
This book is a comprehensive overview of advancements in artificial intelligence (AI) and how it can be applied in the field of air quality management. It explains the linkage between conventional approaches used in air quality monitoring and AI techniques such as data collection and preprocessing, deep learning, machine vision, natural language processing, and ensemble methods. The integration of climate models and AI enables readers to understand the relationship between air quality and climate change. Different case studies demonstrate the application of various air monitoring and prediction methodologies and their effectiveness in addressing real-world air quality challenges. Features A thorough coverage of air quality monitoring and prediction techniques. In-depth evaluation of cutting-edge AI techniques such as machine learning and deep learning. Diverse global perspectives and approaches in air quality monitoring and prediction. Practical insights and real-world case studies from different monitoring and prediction techniques. Future directions and emerging trends in AI-driven air quality monitoring. This is a great resource for professionals, researchers, and students interested in air quality management and control in the fields of environmental science and engineering, atmospheric science and meteorology, data science, and AI.
Using Ai For Small Business Forecasting
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Author : Minghai Zheng
language : en
Publisher: Independently Published
Release Date : 2023-07-11
Using Ai For Small Business Forecasting written by Minghai Zheng and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-11 with categories.
1. #SmallBusinessForecasting #AIPredictions Looking to improve your small business forecasting? "Using AI for Small Business Forecasting" provides practical insights and strategies for using AI to make more accurate predictions. 2. #AIinBusiness #PredictiveAnalytics Discover the latest trends in AI-based predictive analytics with "Using AI for Small Business Forecasting." This book offers a comprehensive guide to leveraging AI to improve your business forecasting. 3. #AccuratePredictions #SmallBusinessSuccess Make more accurate predictions and achieve small business success with AI-powered forecasting tools. Get "Using AI for Small Business Forecasting" and take your business to the next level! 4. #SmartBusinessTools #AIForecasting Looking for smart business tools that can help you forecast accurately? Check out "Using AI for Small Business Forecasting" and discover the latest AI-based forecasting techniques. 5. #DataAnalysis #SmallBusinessGrowth Leverage AI to analyze data and fuel small business growth! "Using AI for Small Business Forecasting" provides insights and strategies for using AI to make better business decisions. Small businesses face a variety of challenges when it comes to forecasting, from predicting cash flow and sales trends to assessing market demand and identifying growth opportunities. Traditional forecasting methods can be time-consuming and prone to error, leaving small business owners and managers struggling to make informed decisions. Fortunately, with the rise of artificial intelligence (AI), small businesses now have access to powerful tools that can help them make more accurate predictions and optimize their operations. "Using AI for Small Business Forecasting: Techniques and Strategies for More Accurate Predictions" is a comprehensive guide to leveraging AI-powered predictive analytics tools to improve your small business forecasting. This book provides a practical overview of how AI-based tools can be used to analyze data, identify trends, and generate predictive models that can help small businesses make informed decisions. We'll explore how AI can be used to improve financial forecasting, predict sales trends, assess risk, and identify growth opportunities. The chapters that follow will delve deeper into specific topics related to AI-powered small business forecasting, including selecting the right AI tools and platforms, implementing effective data analysis strategies, and addressing common challenges associated with AI forecasting. Whether you're a small business owner, manager, or analyst, this book offers a wealth of knowledge and tools to help you effectively leverage AI-powered predictive analytics solutions and achieve success. From machine learning and regression models to decision trees and time series analysis, "Using AI for Small Business Forecasting" provides a comprehensive guide to harnessing the power of AI for more accurate predictions. MingHai Zheng is the founder of zhengpublishing.com and lives in Wuhan, China. His main publishing areas are business, management, self-help, computers and other emerging foreword fields.
Techniques And Tools For Artificial Intelligence Automatic Time Series Forecasting Via R
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Author : E Zúñiga
language : en
Publisher: Independently Published
Release Date : 2024-10-17
Techniques And Tools For Artificial Intelligence Automatic Time Series Forecasting Via R written by E Zúñiga and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-17 with Computers categories.
Predictive artificial intelligence (AI) uses machine learning algorithms that rely on historical data to identify patterns, make predictions and forecast trends. It is based on the ability to use statistical analysis to identify patterns, anticipate behaviour and forecast future events. The field of statistics has long been used to make predictions about the future. Predictive AI makes statistical analysis faster and (theoretically) more accurate by machine learning and accessing large amounts of data. While its predictions are not guaranteed to be correct, predictive AI can help businesses prepare for the future and personalise customer experiences. Predictive AI is just one of many capabilities offered by AI, which refers to a set of abilities that computers can have to mimic human cognition. Predictive AI models are designed to accelerate decision making by helping businesses make accurate and informed decisions. Analyse patterns and make predictions by identifying anomalies in data and extrapolating future events. This can reduce the time it takes businesses to research and study information so they can spend more time making strategic decisions. In this book, we will go deeper into the automatic methods of prediction with time series that constitute essential tools in Predictive Artificial Intelligence. ARIMAX models under the Box and Jenkins methodology, state space models and time series models through neural networks are considered. A variety of exercises are developed using the automatic prediction functions of the R software. In addition, the clssical R functions for the treatment of ARIMAX models are presented.