Practical Approaches to Time Series Analysis and Forecasting using Python for Informed Decision-Making KEY FEATURES ● Comprehensive Resource for Python-Based Time Series Analysis and Forecasting. ● Delve into real-world applications with industry-specific case studies. ● Extract valuable insights by solving time series challenges across various sectors. ● Understand the significance of Azure Time Series Insights and AWS Forecast components. ● Practical insights into leveraging cloud platforms for efficient time series forecasting. DESCRIPTION Embark on a transformative journey through the intricacies of time series analysis and forecasting with this comprehensive handbook. Beginning with the essential packages for data science and machine learning projects you will delve into Python's prowess for efficient time series data analysis, exploring the core components and real-world applications across various industries through compelling use-case studies. From understanding classical models like AR, MA, ARMA, and ARIMA to exploring advanced techniques such as exponential smoothing and ETS methods, this guide ensures a deep understanding of the subject. It will help you navigate the complexities of vector autoregression (VAR, VMA, VARMA) and elevate your skills with a deep dive into deep learning techniques for time series analysis. By the end of this book, you will be able to harness the capabilities of Azure Time Series Insights and explore the cutting-edge AWS Forecast components, unlocking the cloud's power for advanced and scalable time series forecasting. WHAT WILL YOU LEARN ● Explore Time Series Data Analysis and Forecasting, covering components and significance. ● Gain a practical understanding through hands-on examples and real-world case studies. ● Master Time Series Models (AR, MA, ARMA, ARIMA, VAR, VMA, VARMA) with executable samples. ● Delve into Deep Learning for Time Series Analysis, demystified with classical examples. ● Actively engage with Azure Time Series Insights and AWS Forecast components for a contemporary perspective. WHO IS THIS BOOK FOR? This book caters to beginners, intermediates, and practitioners in data-related fields such as Data Analysts, Data Scientists, and Machine Learning Engineers, as well as those venturing into Time Series Analysis and Forecasting. It assumes readers have a foundational understanding of programming languages (C, C++, Python), data structures, statistics, and visualization concepts. With a focus on specific projects, it also functions as a quick reference for advanced users. TABLE OF CONTENTS 1. Introduction to Python and its key packages for DS and ML Projects 2. Python for Time Series Data Analysis 3. Time Series Analysis and its Components 4. Time Series Analysis and Forecasting Opportunities in Various Industries 5. Exploring various aspects of Time Series Analysis and Forecasting 6. Exploring Time Series Models - AR, MA, ARMA, and ARIMA 7. Understanding Exponential Smoothing and ETS Methods in TSA 8. Exploring Vector Autoregression and its Subsets (VAR, VMA, and VARMA) 9. Deep Learning for Time Series Analysis and Forecasting 10. Azure Time Series Insights 11. AWSForecast Index

Empowering the Global Workforce with ChatGPT Expertise. KEY FEATURES ● Comprehensive Guide to GPT Evolution, AI Capabilities, and Prompt Engineering. ● Design Patterns for Enterprise Personas, Architectures, and AI Assistants. ● Management of the GPT Solution Development Cycle. DESCRIPTION “Ultimate ChatGPT Handbook for Enterprises” is your indispensable resource for navigating the transformative world of ChatGPT within the enterprise domain. It provides a deep dive into ChatGPT's evolution, capabilities, and its potential to democratize technology interactions through natural language. Throughout its chapters, you'll embark on a journey that spans from comprehending the lineage of GPT models to mastering advanced prompt engineering techniques. It will help you take a step into a futuristic enterprise landscape where ChatGPT seamlessly collaborates with human intelligence, fundamentally transforming daily work routines across various enterprise roles. The latter chapters will help you attain proficiency in managing GPT projects, discovering the agile and iterative approach to GPT solution life cycles using real-world scenarios. You will also be introduced to practical GPT implementation frameworks for both Python and Java. This book offers practical insights and applicable skills, fostering informed dialogue and active participation in the ongoing enterprise AI revolution. If you want to stay at the forefront of the rapidly evolving AI landscape and unlock enterprise excellence through ChatGPT, this book is your go-to companion. WHAT WILL YOU LEARN ● Discover strategies to maximize ChatGPT's capabilities, fostering innovation and process optimization across global industry sectors. ● Develop proficiency in crafting effective prompts using Prompt Engineering for seamless AI interactions, enhancing ChatGPT's utility in enterprise contexts. ● Acquire the expertise to design intelligent assistants that elevate enterprise operations, promoting efficiency and innovation. ● Gain practical skills to implement ChatGPT solutions using Python and Java, enabling seamless integration with your enterprise systems. ● Learn effective project management from initiation to validation and change management, ensuring successful GPT solution implementation in enterprises. ● Explore how ChatGPT can reshape various roles, boosting productivity and fostering harmonious AI-human collaboration in the workplace. WHO IS THIS BOOK FOR? This book is designed for business professionals, IT specialists, and AI enthusiasts who are eager to delve into the transformative world of ChatGPT and its applications in the enterprise landscape. A foundational understanding of AI concepts and familiarity with enterprise dynamics will be beneficial, but not mandatory, as the book is structured to guide readers from basic concepts to advanced implementations, catering to both novices and experts alike. TABLE OF CONTENTS 1. ​​From GPT-1 to ChatGPT-4: The Evolution Towards Generative AI 2. CapabilityGPT An Enterprise AI-Capability Framework for ChatGPT 3. The Impact of ChatGPT on the Enterprise 4. Architecture Patterns enabled by GPT-Models 5. Advanced GPT Prompt Engineering Techniques 6. Designing Prompt-based Intelligent Assistants 7. Mastery of GPT-Projects 8. LangChain: GPT Implementation Framework for Python 9. predictive-powers: GPT Implementation Framework for Java APPENDIX A: APPENDIX B:

Innovative Computing for Green Technologies aims to introduce the latest technologies and promote international collaborations across the scientific community and, eventually, the general public. It consists of 3 sections. Each section explores how technologies give real-time responses to current environmental, technological, social and economic challenges, which can be beneficial in reducing carbon emissions, improving resource utilization, promoting active engagement of citizens and a sustainable environment. Multi-layer provincial system of innovation is made up of digital networks, individual intellectual capital and the social capital of the city, which together constitute collective intelligence. Economic competitiveness and innovation achieved through the knowledge-based economy mark a city as intelligent, allowing it to generate a ‘spatial competitive advantage’ through industrial districts, regions and learning clusters supported by digital networks and artificial intelligence. A technophile will love this book as it presents the solution to face challenges and advanced concepts to make technology more feasible.

This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Technologies for Interactive Entertainment, INTETAIN 2021. Due to the COVID-19 global pandemic, the conference was held virtually. This year's conference put an emphasis on the future of technologies for interactive entertainment. The 25 full papers presented at the conference and thus included in this book were selected from 50 submissions, and present novel and innovative work in areas including art, science, design and engineering regarding computer-based systems or devices that provide intelligent human interaction, learning, or entertainment experiences. The papers are grouped in 2 sessions of thematic issues on emerging applications and augmented reality.

Implement real-world machine learning in a microservices architecture as well as design, build, and deploy intelligent microservices systems using examples and case studies Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesDesign, build, and run microservices systems that utilize the full potential of machine learningDiscover the latest models and techniques for combining microservices and machine learning to create scalable systemsImplement machine learning in microservices architecture using open source applications with pros and consBook Description With the rising need for agile development and very short time-to-market system deployments, incorporating machine learning algorithms into decoupled fine-grained microservices systems provides the perfect technology mix for modern systems. Machine Learning in Microservices is your essential guide to staying ahead of the curve in this ever-evolving world of technology. The book starts by introducing you to the concept of machine learning microservices architecture (MSA) and comparing MSA with service-based and event-driven architectures, along with how to transition into MSA. Next, you'll learn about the different approaches to building MSA and find out how to overcome common practical challenges faced in MSA design. As you advance, you'll get to grips with machine learning (ML) concepts and see how they can help better design and run MSA systems. Finally, the book will take you through practical examples and open source applications that will help you build and run highly efficient, agile microservices systems. By the end of this microservices book, you'll have a clear idea of different models of microservices architecture and machine learning and be able to combine both technologies to deliver a flexible and highly scalable enterprise system. What you will learnRecognize the importance of MSA and ML and deploy both technologies in enterprise systemsExplore MSA enterprise systems and their general practical challengesDiscover how to design and develop microservices architectureUnderstand the different AI algorithms, types, and models and how they can be applied to MSAIdentify and overcome common MSA deployment challenges using AI and ML algorithmsExplore general open source and commercial tools commonly used in MSA enterprise systemsWho this book is for This book is for machine learning solution architects, system and machine learning developers, and system and solution integrators of private and public sector organizations. Basic knowledge of DevOps, system architecture, and artificial intelligence (AI) systems is assumed, and working knowledge of the Python programming language is highly desired.

This book full-color textbook assumes a basic understanding of statistics and mathematical or statistical modeling. Although a little programming experience would be nice, but it is not required. We use current real-world data, like COVID-19, to motivate times series analysis have three thread problems that appear in nearly every chapter: "Got Milk?", "Got a Job?" and "Where's the Beef?" Chapter 1: Loading data in the R-Studio and Jupyter Notebook environments. Chapter 2: Components of a times series and decomposition Chapter 3: Moving averages (MAs) and COVID-19 Chapter 4: Simple exponential smoothing (SES), Holt's and Holt-Winter's double and triple exponential smoothing Chapter 5: Python programming in Jupyter Notebook for the concepts covered in Chapters 2, 3 and 4 Chapter 6: Stationarity and differencing, including unit root tests. Chapter 7: ARIMA and SARMIA (seasonal) modeling and forecast development Chapter 8: ARIMA modeling using Python Chapter 9: Structural models and analysis using unobserved component models (UCMs) Chapter 10: Advanced time series analysis, including time-series interventions, exogenous regressors, and vector autoregressive (VAR) processes.

The pervasiveness of computers in every field of science, industry and everyday life has meant that applied mathematics, particularly in relation to modeling and simulation, has become ever more important in recent years. This book presents the proceedings of the 2021 International Conference on Applied Mathematics, Modeling and Computer Simulation (AMMCS 2021), hosted in Wuhan, China, and held as a virtual event from 13 to 14 November 2021. The aim of the conference is to foster the knowledge and understanding of recent advances across the broad fields of applied mathematics, modeling and computer simulation, and it provides an annual platform for scholars and researchers to communicate important recent developments in their areas of specialization to colleagues and other scientists in related disciplines. This year more than 150 participants were able to exchange knowledge and discuss recent developments via the conference. The book contains 115 peer-reviewed papers, selected from more than 250 submissions and ranging from the theoretical and conceptual to the strongly pragmatic and all addressing industrial best practice. Topics covered include mathematical modeling and applications, engineering applications and scientific computations, and the simulation of intelligent systems. Providing an overview of recent development and with a mix of practical experiences and enlightening ideas, the book will be of interest to researchers and practitioners everywhere.

This multi-contributed handbook focuses on the latest workings of IoT (internet of Things) and Big Data. As the resources are limited, it's the endeavor of the authors to support and bring the information into one resource. The book is divided into 4 sections that covers IoT and technologies, the future of Big Data, algorithms, and case studies showing IoT and Big Data in various fields such as health care, manufacturing and automation. Features Focuses on the latest workings of IoT and Big Data Discusses the emerging role of technologies and the fast-growing market of Big Data Covers the movement toward automation with hardware, software, and sensors, and trying to save on energy resources Offers the latest technology on IoT Presents the future horizons on Big Data