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.

What makes seasoned IT professionals run for cover? Answer: Forecasting Oracle Performance! Craig Shallahamer is an Oracle performance expert with over 18 years of experience. His book is the first to focus not on the problem of solving today's problem, but squarely on the problem of forecasting the future performance of an Oracle database. Other Oracle performance books are good for putting out fires; Craig's book helps you avoid all the heat in the first place. If you’re an IT practioner who appreciates application over mathematical proofs than you’ll be pleasantly surprised! Each chapter is filled with examples to transform the theory, mathematics, and methods into something you can practically apply. Craig's goal is to teach you about real-word Oracle performance forecasting. Period. There is no hidden agenda. This book is a kind of training course. After reading, studying, and practicing the material covered in this book, you to be able to confidently, responsibly, and professionally forecast performance and system capacity in a wide variety of real-life situations. If you are more management-minded (or want to be), you will be delighted with the service level management focus. Forecasting makes good business sense because it maximizes the return on IT investment and minimizes unplanned down time. To those who think forecasting is a waste of money: well...obviously, they’ve never been on the evening news because their company lost millions of dollars in revenue and brand destruction because of poorly performing or unavailable systems. Without a doubt, you will be equipped to deal with the realities of forecasting Oracle performance. But this book gives you more. Not only will you receive a technical and mathematical perspective, but also a communication, a presentation, and a management perspective. This is career building stuff and immensely satisfying! What you’ll learn This book is a “how-to” book filled with examples to transform theory and mathematics into something you can practically apply. You will learn how to use a variety of forecasting models, which will enable you to methodically: Help manage service levels from a business value perspective Identify the risk of over utilized resources Predict what component of an architecture is at risk Predict when a system will be at risk Develop multiple risk mitigating strategies to ensure service levels are maintained Characterize a complex Oracle workload Who this book is for IT professionals who must ensure their production Oracle systems are meeting service levels, in part, through forecasting performance, identifying risk, and developing solutions to ensure systems are available without wasting budget. Readers include database administrators, IT managers, developers, capacity planners, systems architects, systems integrators.

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 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.