Download or read book Disaggregating Times Series Data written by and published by . This book was released on 1997 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes our experiences with disaggregating time series data. Suppose we have gathered data every two seconds and want to guess the data at one-second intervals. Under certain assumptions, there are several reasonable disaggregation methods as well as several performance measures to judge their performance. Here we present results for both simulated and real data for two methods using several performance criteria.
Download or read book Disaggregating Time Series Data for Energy Consumption by Aggregate and Individual Customer written by Steven Vitullo and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation generalizes the problem of disaggregating time series data and describes the disaggregation problem as a mathematical inverse problem that breaks up aggregated (measured) time series data that is accumulated over an interval and estimates its component parts. We describe five different algorithms for disaggregating time series data: the Naive, Time Series Reconstruction (TSR), Piecewise Linear Optimization (PLO), Time Series Reconstruction with Resampling (RS), and Interpolation (INT). The TSR uses least squares and domain knowledge of underlying correlated variables to generate underlying estimates and handles arbitrarily aggregated time steps and non-uniformly aggregated time steps. The PLO performs an adjustment on underlying estimates so the sum of the underlying estimated data values within an interval are equal to the aggregated data value. The RS repeatedly samples a subset of our data, and the fifth algorithm uses an interpolation to estimate underlying estimated data values. Several methods of combining these algorithms, taken from the forecasting domain, are applied to improve the accuracy of the disaggregated time series data. We evaluate our component and ensemble algorithms in three different applications: disaggregating aggregated (monthly) gas consumption into disaggregated (daily) gas consumption from natural gas regional areas (operating areas), disaggregating United States Gross Domestic Product (GDP) from yearly GDP to quarterly GDP, and forecasting when a truck should fill a customer's heating oil tank. We show our five algorithms successfully used to disaggregate historical natural gas consumption and GDP, and we show combinations of these algorithms can improve further the magnitude and variability of the natural gas consumption or GDP series. We demonstrate that the PLO algorithm is the best of the Naive, TSR, and PLO algorithms when disaggregating GDP series. Finally, ex-post results using the Naive, TSR, PLO, RS, INT, and the ensemble algorithms when applied to forecast heating oil deliveries are shown. Results show the Equal Weight (EW) combination of the Naive, TSR, PLO, RS, and INT algorithms outperforms the forecasting system Company YOU used before approaching the gasdayTM laboratory at Marquette University, and comes close, but does not outperform existing techniques the GasDayTM laboratory has implemented to forecast heating oil deliveries.
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 Modeling Financial Time Series with S PLUS written by Eric Zivot and published by Springer Science & Business Media. This book was released on 2007-10-10 with total page 998 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. It is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This edition covers S+FinMetrics 2.0 and includes new chapters.
Download or read book A Statistical Approach for Disaggregating Mixed frequency Economic Time Series Data written by Wai-Sum Chan and published by . This book was released on 1997 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The problem of mixed-frequency time series data arises from changing the observation frequency. For example, we may have a time series with quarterly observations in the first portion and annual figures in the remainder. We shall call that quarter-year mixed-frequency data. In this paper we suggest a method to disaggregate the annual observations to quarterly values. The proposed method can easily be generalised to the year-quarter, quarter-month, year-month and other mixed-frequency situations; it may avoid difficulties of time series modelling and is easy to implement. A step-by-step algorithm of the method is given so that econometricians not expert in this area can still perform the procedure. The proposed method is illustrated through two real examples. We also conduct a small scale Monte Carlo experiment to compare the proposed procedure with two existing alternative methods. Finally, some concluding remarks are given"--Abstract.
Download or read book Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization written by J.B. Marco and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic hydrology is an essential base of water resources systems analysis, due to the inherent randomness of the input, and consequently of the results. These results have to be incorporated in a decision-making process regarding the planning and management of water systems. It is through this application that stochastic hydrology finds its true meaning, otherwise it becomes merely an academic exercise. A set of well known specialists from both stochastic hydrology and water resources systems present a synthesis of the actual knowledge currently used in real-world planning and management. The book is intended for both practitioners and researchers who are willing to apply advanced approaches for incorporating hydrological randomness and uncertainty into the simulation and optimization of water resources systems. (abstract) Stochastic hydrology is a basic tool for water resources systems analysis, due to inherent randomness of the hydrologic cycle. This book contains actual techniques in use for water resources planning and management, incorporating randomness into the decision making process. Optimization and simulation, the classical systems-analysis technologies, are revisited under up-to-date statistical hydrology findings backed by real world applications.
Download or read book Applied Modeling of Hydrologic Time Series written by Jose D. Salas and published by Water Resources Publication. This book was released on 1980 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book On the Extrapolation with the Denton Proportional Benchmarking Method written by Mr.Tommaso Di Fonzo and published by International Monetary Fund. This book was released on 2012-06-01 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical offices have often recourse to benchmarking methods for compiling quarterly national accounts (QNA). Benchmarking methods employ quarterly indicator series (i) to distribute annual, more reliable series of national accounts and (ii) to extrapolate the most recent quarters not yet covered by annual benchmarks. The Proportional First Differences (PFD) benchmarking method proposed by Denton (1971) is a widely used solution for distribution, but in extrapolation it may suffer when the movements in the indicator series do not match consistently the movements in the target annual benchmarks. For this reason, an enhanced formula for extrapolation was recommended by the IMF’s Quarterly National Accounts Manual: Concepts, Data Sources, and Compilation (2001). We discuss the rationale behind this technique, and propose a matrix formulation of it. In addition, we present applications of the enhanced formula to artificial and real-life benchmarking examples showing how the extrapolations for the most recent quarters can be improved.
Download or read book Time Series and Panel Data Econometrics written by M. Hashem Pesaran and published by Oxford University Press. This book was released on 2015 with total page 1095 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides a rigorous, nevertheless user-friendly, account of the time series techniques dealing with univariate and multivariate time series models, as well as panel data models. It is distinct from other time series texts in the sense that it also covers panel data models and attempts at a more coherent integration of time series, multivariate analysis, and panel data models. It builds on the author's extensive research in the areas of time series and panel data analysis and covers a wide variety of topics in one volume. Different parts of the book can be used as teaching material for a variety of courses in econometrics. It can also be used as reference manual. It begins with an overview of basic econometric and statistical techniques, and provides an account of stochastic processes, univariate and multivariate time series, tests for unit roots, cointegration, impulse response analysis, autoregressive conditional heteroskedasticity models, simultaneous equation models, vector autoregressions, causality, forecasting, multivariate volatility models, panel data models, aggregation and global vector autoregressive models (GVAR). The techniques are illustrated using Microfit 5 (Pesaran and Pesaran, 2009, OUP) with applications to real output, inflation, interest rates, exchange rates, and stock prices.
Download or read book Practical Guidebook on Data Disaggregation for the Sustainable Development Goals written by Asian Development Bank and published by Asian Development Bank. This book was released on 2021-05-01 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: The "leave no one behind" principle espoused by the 2030 Agenda for Sustainable Development requires measures of progress for different segments of the population. This entails detailed disaggregated data to identify subgroups that might be falling behind, to ensure progress toward achieving the Sustainable Development Goals (SDGs). The Asian Development Bank and the Statistics Division of the United Nations Department of Economic and Social Affairs developed this practical guidebook with tools to collect, compile, analyze, and disseminate disaggregated data. It also provides materials on issues and experiences of countries regarding data disaggregation for the SDGs. This guidebook is for statisticians and analysts from planning and sector ministries involved in the production, analysis, and communication of disaggregated data.
Download or read book Time Series for Data Scientists written by Juana Sanchez and published by Cambridge University Press. This book was released on 2023-04-30 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: A user-friendly, introductory, learning-by-doing bridge between classical and machine learning time series analysis with R.
Download or read book Time Series written by David R. Brillinger and published by SIAM. This book was released on 1981-01-01 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for students and researchers, this text employs basic techniques of univariate and multivariate statistics for the analysis of time series and signals. It provides a broad collection of theorems, placing the techniques on firm theoretical ground. The techniques, which are illustrated by data analyses, are discussed in both a heuristic and a formal manner, making the book useful for both the applied and the theoretical worker. An extensive set of original exercises is included. Time Series: Data Analysis and Theory takes the Fourier transform of a stretch of time series data as the basic quantity to work with and shows the power of that approach. It considers second- and higher-order parameters and estimates them equally, thereby handling non-Gaussian series and nonlinear systems directly. The included proofs, which are generally short, are based on cumulants. Audience: this book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical. Readers should have some background in complex function theory and matrix algebra and should have successfully completed the equivalent of an upper division course in statistics.
Download or read book Asymptotic Behavior of Time Series Aggregates written by G. C. Tiao and published by . This book was released on 1972 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper discusses the efficiency of disaggregation in forecasting time series aggregates. Let be the disaggregated series and XT+(ZMT-m+1 + ... + ZMT) be the m-component aggregated series. Forecasts of future XT may be constructed from data on (i) Zt or (ii) XT. It is shown that, for large m, there is no gain in using the disaggreagated data if Zt is stationary, but dramatic gain can be obtained when Zt is non-stationary. (Author).
Download or read book Time Series Analysis Univariate and Multivariate Methods written by William W. S. Wei and published by Pearson. This book was released on 2018-03-14 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications. Time Series Analysis is a thorough introduction to both time-domain and frequency-domain analyses of univariate and multivariate time series methods, with coverage of the most recently developed techniques in the field.
Download or read book Disaggregation in Econometric Modelling Routledge Revivals written by Terry Barker and published by Routledge. This book was released on 2014-02-04 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, first published in 1990, leading theorists and applied economists address themselves to the key questions of aggregation. The issues are covered both theoretically and in wide-ranging applications. Of particular intrest is the optimal aggregation of trade data, the need for micro-modelling when imoprtant non-linearities are present (for example, tax exhaustion in modelling company behaviour) and the use of a micro-model to stimulate labour supply behaviour in a macro-model of the Netherlands.
Download or read book Advances in Time Series Data Methods in Applied Economic Research written by Nicholas Tsounis and published by Springer. This book was released on 2018-12-12 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: This conference proceedings volume presents advanced methods in time series estimation models that are applicable various areas of applied economic research such as international economics, macroeconomics, microeconomics, finance economics and agricultural economics. Featuring contributions presented at the 2018 International Conference on Applied Economics (ICOAE) held in Warsaw, Poland, this book presents contemporary research using applied econometric method for analysis as well as country specific studies with potential implications on economic policy. Applied economics is a rapidly growing field of economics that combines economic theory with econometrics to analyse economic problems of the real world usually with economic policy interest. ICOAE is an annual conference started in 2008 with the aim to bring together economists from different fields of applied economic research in order to share methods and ideas. Approximately 150 papers are submitted each year from about 40 countries around the world. The goal of the conference and the enclosed papers is to allow for an exchange of experiences with different applied econometric methods and to promote joint initiatives among well-established economic fields such as finance, agricultural economics, health economics, education economics, international trade theory and management and marketing strategies. Featuring global contributions, this book will be of interest to researchers, academics, professionals and policy makers in the field of applied economics and econometrics.
Download or read book Practical Time Series Analysis written by Aileen Nielsen and published by O'Reilly Media. This book was released on 2019-09-20 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
