Forecasting: principles and practice


Book Description

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.







Demand Prediction in Retail


Book Description

From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.







Predicting the Future


Book Description

The future obviously matters to us. It is, after all, where we'll be spending the rest of our lives. We need some degree of foresight if we are to make effective plans for managing our affairs. Much that we would like to know in advance cannot be predicted. But a vast amount of successful prediction is nonetheless possible, especially in the context of applied sciences such as medicine, meteorology, and engineering. This book examines our prospects for finding out about the future in advance. It addresses questions such as why prediction is possible in some areas and not others; what sorts of methods and resources make successful prediction possible; and what obstacles limit the predictive venture. Nicholas Rescher develops a general theory of prediction that encompasses its fundamental principles, methodology, and practice and gives an overview of its promises and problems. Predicting the Future considers the anthropological and historical background of the predictive enterprise. It also examines the conceptual, epistemic, and ontological principles that set the stage for predictive efforts. In short, Rescher explores the basic features of the predictive situation and considers their broader implications in science, in philosophy, and in the management of our daily affairs.







Time Predictions


Book Description

This book is published open access under a CC BY 4.0 license. Predicting the time needed to complete a project, task or daily activity can be difficult and people frequently underestimate how long an activity will take. This book sheds light on why and when this happens, what we should do to avoid it and how to give more realistic time predictions. It describes methods for predicting time usage in situations with high uncertainty, explains why two plus two is usually more than four in time prediction contexts, reports on research on time prediction biases, and summarizes the evidence in support of different time prediction methods and principles. Based on a comprehensive review of the research, it is the first book summarizing what we know about judgment-based time predictions. Large parts of the book are directed toward people wishing to achieve better time predictions in their professional life, such as project managers, graphic designers, architects, engineers, film producers, consultants, software developers, or anyone else in need of realistic time usage predictions. It is also of benefit to those with a general interest in judgment and decision-making or those who want to improve their ability to predict and plan ahead in daily life.







Data Science for Supply Chain Forecasting


Book Description

Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting. This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves. This hands-on book, covering the entire range of forecasting—from the basics all the way to leading-edge models—will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting.




Monthly Checklist of State Publications


Book Description

June and Dec. issues contain listings of periodicals.