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Forecasting: principles and practice
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.
Intelligent Systems Modeling and Simulation II
This book develops a new system of modeling and simulations based on intelligence system. As we are directly moving from Third Industrial Revolution (IR3.0) to Fourth Industrial Revolution (IR4.0), there are many emergence techniques and algorithm that appear in many sciences and engineering branches. Nowadays, most industries are using IR4.0 in their product development as well as to refine their products. These include simulation on oil rig drilling, big data analytics on consumer analytics, fastest algorithm for large-scale numerical simulations and many more. These will save millions of dollar in the operating costs. Without any doubt, mathematics, statistics and computing are well blend...
Time Series for Data Scientists
Learn by doing with this user-friendly introduction to time series data analysis in R. This book explores the intricacies of managing and cleaning time series data of different sizes, scales and granularity, data preparation for analysis and visualization, and different approaches to classical and machine learning time series modeling and forecasting. A range of pedagogical features support students, including end-of-chapter exercises, problems, quizzes and case studies. The case studies are designed to stretch the learner, introducing larger data sets, enhanced data management skills, and R packages and functions appropriate for real-world data analysis. On top of providing commented R programs and data sets, the book's companion website offers extra case studies, lecture slides, videos and exercise solutions. Accessible to those with a basic background in statistics and probability, this is an ideal hands-on text for undergraduate and graduate students, as well as researchers in data-rich disciplines
Writing the Goodlife
"The book looks to long-established traditions of environmentalist thought alive in Mexican American literary history over the last 150 years"--Provided by publisher.
Practical Time Series Analysis
Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance
Forecasting
Concise, engaging, and highly intuitive—this accessible guide equips you with an understanding of all the basic principles of forecasting Making accurate predictions about the economy has always been difficult, as F. A. Hayek noted when accepting his Nobel Prize in economics, but today forecasters have to contend with increasing complexity and unpredictable feedback loops. In this accessible and engaging guide, David Hendry, Michael Clements, and Jennifer Castle provide a concise and highly intuitive overview of the process and problems of forecasting. They explain forecasting concepts including how to evaluate forecasts, how to respond to forecast failures, and the challenges of forecasting accurately in a rapidly changing world. Topics covered include: What is a forecast? How are forecasts judged? And how can forecast failure be avoided? Concepts are illustrated using real-world examples including financial crises, the uncertainty of Brexit, and the Federal Reserve’s record on forecasting. This is an ideal introduction for university students studying forecasting, practitioners new to the field and for general readers interested in how economists forecast.
Machine Learning Toolbox for Social Scientists
Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data...
Weep Not, Child
"Two small boys stand on a rubbish heap and look into the future. One boy is excited, he is beginning school; the other, his brother, is an apprentice carpetner. Together, they will serve their country--the teacher and the craftsman. But this is Kenya and times are against them. In the forests, the Mau Mau are waging war against the white government, and two brothers, Njoroge and Kamau, and the rest of their family, need to decide where their loyalties lie. For the practical man, the choice is simple, but for Njoroge, the scholar, the dream of progress through learning is a hard one to give up"--P. [4] of cover.
An Introduction to Business Analytics
Business Analytics (BA) is about turning data into decisions. This book covers the full range of BA topics, including statistics, machine learning and optimization, in a way that makes them accessible to a broader audience. Decision makers will gain enough insight into the subject to have meaningful discussions with machine learning specialists, and those starting out as data scientists will benefit from an overview of the field and take their first steps as business analytics specialist. Through this book and the various exercises included, you will be equipped with an understanding of BA, while learning R, a popular tool for statistics and machine learning.
Julia Programming Projects
A step-by-step guide that demonstrates how to build simple-to-advanced applications through examples in Julia Lang 1.x using modern tools Key FeaturesWork with powerful open-source libraries for data wrangling, analysis, and visualizationDevelop full-featured, full-stack web applications Learn to perform supervised and unsupervised machine learning and time series analysis with JuliaBook Description Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. ...
