Download or read book Forecasting principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Forecasting Structural Time Series Models and the Kalman Filter written by Andrew C. Harvey and published by Cambridge University Press. This book was released on 1990-02-22 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Download or read book Mathematical Problems and Methods of Hydrodynamic Weather Forecasting written by Vladimir Gordin and published by CRC Press. This book was released on 2000-09-20 with total page 846 pages. Available in PDF, EPUB and Kindle. Book excerpt: The material provides an historical background to forecasting developments as well as introducing recent advances. The book will be of interest to both mathematicians and physicians, the topics covered include equations of dynamical meteorology, first integrals, non-linear stability, well-posedness of boundary problems, non-smooth solutions, parameters and free oscillations, meteorological data processing, methods of approximation and interpolation and numerical methods for forecast modelling.

Download or read book Time Series Forecasting written by Douglas C. Montgomery and published by John Wiley & Sons. This book was released on 2024-07-30 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bring the latest statistical tools to bear on predicting future variables and outcomes A huge range of fields rely on forecasts of how certain variables and causal factors will affect future outcomes, from product sales to inflation rates to demographic changes. Time series analysis is the branch of applied statistics which generates forecasts, and its sophisticated use of time oriented data can vastly impact the quality of crucial predictions. The latest computing and statistical methodologies are constantly being sought to refine these predictions and increase the confidence with which important actors can rely on future outcomes. Time Series Analysis and Forecasting presents a comprehensive overview of the methodologies required to produce these forecasts with the aid of time-oriented data sets. The potential applications for these techniques are nearly limitless, and this foundational volume has now been updated to reflect the most advanced tools. The result, more than ever, is an essential introduction to a core area of statistical analysis. Readers of the third edition of Time Series Analysis and Forecasting will also find: Updates incorporating JMP, SAS, and R software, with new examples throughout Over 300 exercises and 50 programming algorithms that balance theory and practice Supplementary materials in the e-book including solutions to many problems, data sets, and brand-new explanatory videos covering the key concepts and examples from each chapter. Time Series Analysis and Forecasting is ideal for graduate and advanced undergraduate courses in the areas of data science and analytics and forecasting and time series analysis. It is also an outstanding reference for practicing data scientists.

Download or read book Introduction to Time Series and Forecasting written by Peter J. Brockwell and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

Download or read book Operational Flood Forecasting Warning and Response for Multi Scale Flood Risks in Developing Cities written by María Carolina Rogelis and published by CRC Press. This book was released on 2020-04-22 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to contribute to understanding risk knowledge and to forecasting components of early flood warning, particularly in the environment of tropical high mountains in developing cities. This research covers a challenge, taking into account the persistent lack of data, limited resources and often complex climatic, hydrologic and hydraulic conditions. In this research, a regional method is proposed for assessing flash flood susceptibility and for identifying debris flow predisposition at the watershed scale. An indication of hazard is obtained from the flash flood susceptibility analysis and continually, the vulnerability and an indication of flood risk at watershed scale was obtained. Based on risk analyses, the research follows the modelling steps for flood forecasting development. Input precipitation is addressed in the environment of complex topography commonly found in mountainous tropical areas. A distributed model, a semi-distributed model and a lumped model were all used to simulate the discharges of a tropical high mountain basin with a páramo upper basin. Performance analysis and diagnostics were carried out in order to identify the most appropriate model for the study area for flood early warning. Finally, the Weather Research and Forecasting (WRF) model was used to explore the added value of numerical weather models for flood early warning in a páramo area.

Download or read book Fresh Perspectives Human Resource Management written by and published by Pearson South Africa. This book was released on 2006 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Simple Forecasting Accuracy Criterion Under Rational Expectations written by José M. Barrionuevo and published by . This book was released on 1992 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Program to Test Skill in Terminal Forecasting written by Irving I. Gringorten and published by . This book was released on 1955 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Notes on Analysis and Severe storm Forecasting Procedures of the Military Weather Warning Center written by Robert C. Miller and published by . This book was released on 1967 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Forecasting Time Series Data with Facebook Prophet written by Greg Rafferty and published by Packt Publishing Ltd. This book was released on 2021-03-12 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using Python Key Features Learn how to use the open-source forecasting tool Facebook Prophet to improve your forecasts Build a forecast and run diagnostics to understand forecast quality Fine-tune models to achieve high performance, and report that performance with concrete statistics Book Description Prophet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your first model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments. By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code. What you will learn Gain an understanding of time series forecasting, including its history, development, and uses Understand how to install Prophet and its dependencies Build practical forecasting models from real datasets using Python Understand the Fourier series and learn how it models seasonality Decide when to use additive and when to use multiplicative seasonality Discover how to identify and deal with outliers in time series data Run diagnostics to evaluate and compare the performance of your models Who this book is for This book is for data scientists, data analysts, machine learning engineers, software engineers, project managers, and business managers who want to build time series forecasts in Python. Working knowledge of Python and a basic understanding of forecasting principles and practices will be useful to apply the concepts covered in this book more easily.

Download or read book Forecasting Time Series Data with Prophet written by Greg Rafferty and published by Packt Publishing Ltd. This book was released on 2023-03-31 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create and improve fully automated forecasts for time series data with strong seasonal effects, holidays, and additional regressors using Python Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore Prophet, the open source forecasting tool developed at Meta, to improve your forecasts Create a forecast and run diagnostics to understand forecast quality Fine-tune models to achieve high performance and report this performance with concrete statistics Book DescriptionForecasting Time Series Data with Prophet will help you to implement Prophet's cutting-edge forecasting techniques to model future data with high accuracy using only a few lines of code. This second edition has been fully revised with every update to the Prophet package since the first edition was published two years ago. An entirely new chapter is also included, diving into the mathematical equations behind Prophet's models. Additionally, the book contains new sections on forecasting during shocks such as COVID, creating custom trend modes from scratch, and a discussion of recent developments in the open-source forecasting community. You'll cover advanced features such as visualizing forecasts, adding holidays and trend changepoints, and handling outliers. You'll use the Fourier series to model seasonality, learn how to choose between an additive and multiplicative model, and understand when to modify each model parameter. Later, you'll see how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models in production. By the end of this book, you'll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code.What you will learn Understand the mathematics behind Prophet’s models Build practical forecasting models from real datasets using Python Understand the different modes of growth that time series often exhibit Discover how to identify and deal with outliers in time series data Find out how to control uncertainty intervals to provide percent confidence in your forecasts Productionalize your Prophet models to scale your work faster and more efficiently Who this book is forThis book is for business managers, data scientists, data analysts, machine learning engineers, and software engineers who want to build time-series forecasts in Python or R. To get the most out of this book, you should have a basic understanding of time series data and be able to differentiate it from other types of data. Basic knowledge of forecasting techniques is a plus.

Download or read book Machine Learning for Time Series Forecasting with Python written by Francesca Lazzeri and published by John Wiley & Sons. This book was released on 2020-12-01 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Download or read book River Forecasting Methods written by Ray K. Linsley and published by . This book was released on 1942 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book INTERNATIONAL JOURNAL OF FORECASTING written by ROBERT FILDES and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Algorithmic Learning Theory written by Marcus Hutter and published by Springer. This book was released on 2007-10-11 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007, held in Sendai, Japan, October 1-4, 2007, co-located with the 10th International Conference on Discovery Science, DS 2007. The 25 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 50 submissions. They are dedicated to the theoretical foundations of machine learning.

Download or read book Monthly Weather Review written by and published by . This book was released on 1989 with total page 940 pages. Available in PDF, EPUB and Kindle. Book excerpt: