Time Series Models for Short-Term Forecasting Performance Indicators
Time Series Models for Short-Term Forecasting Performance Indicators

Author: Arno Palmrich

Publisher: GRIN Verlag

Published: 2009-09-30

Total Pages: 86

ISBN-13: 3640435869

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Diploma Thesis from the year 2007 in the subject Business economics - Business Management, Corporate Governance, grade: highest grade (ausgezeichnet), University of Applied Sciences Kufstein Tirol, course: Economics Statistics, language: English, abstract: Managers use forecasting in budgeting time and resources. In this thesis, various advanced time series models are constructed, computed and tested for adequacy. This thesis serves as a practical guide to regression and time series analysis. It seeks to demonstrate how to approach problems according to scientific standards to students who are familiar with SPSS® but beginners in regression and time series analysis. Bibliographic notes of classical works and more recent academic advances in time series analysis are provided throughout the text. The research question that this thesis seeks to answer can be formulated in its shortest version as: “How can the management of Dalian Chemson Chemical Products Co; Ltd. use existing company data to make short-term predictions about net sales, Cost of Goods Sold (COGS), and net contribution?” More specifically, this thesis seeks to provide different tools (models) for forecasting the P&L entries net sales, COGS, and net contribution a few months ahead. This author’s approach is based on various versions of two models: One model will forecast net sales and the other model will predict COGS. The expected net contribution is simply defined as the difference between the predictions of these two models. In chapter 4.3 an ordinary least squares regression version of the two models has been computed. In chapter 4.6 a weighted least squares regression has been applied to the models. Autoregressions have been computed in chapter 4.7.1 and two Autoregressive Integrated Moving Average (ARIMA) versions have been constructed in chapter 4.7.6. The various versions of the models have then been compared against each other. The version that fits the data best will be used in forecasting. The statistical models in this thesis are computed using SPSS BaseTM, SPSS Regression ModelsTM and SPSS TrendsTM, versions 11.5.0. Each of the model versions constructed herein can be applied in a simple Excel spreadsheet. In the last chapter, a one-step-ahead forecast is produced via the in this thesis developed concept which consists of the most precise versions of the models to forecast net sales and COGS. The forecasting concept developed in this thesis is good in that it produces precise forecasts. Its simplified framework minimizes the effort and expertise required to obtain predictions.

Renewable Energy Forecasting
Renewable Energy Forecasting

Author: Georges Kariniotakis

Publisher: Woodhead Publishing

Published: 2017-09-29

Total Pages: 388

ISBN-13: 0081005059

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Renewable Energy Forecasting: From Models to Applications provides an overview of the state-of-the-art of renewable energy forecasting technology and its applications. After an introduction to the principles of meteorology and renewable energy generation, groups of chapters address forecasting models, very short-term forecasting, forecasting of extremes, and longer term forecasting. The final part of the book focuses on important applications of forecasting for power system management and in energy markets. Due to shrinking fossil fuel reserves and concerns about climate change, renewable energy holds an increasing share of the energy mix. Solar, wind, wave, and hydro energy are dependent on highly variable weather conditions, so their increased penetration will lead to strong fluctuations in the power injected into the electricity grid, which needs to be managed. Reliable, high quality forecasts of renewable power generation are therefore essential for the smooth integration of large amounts of solar, wind, wave, and hydropower into the grid as well as for the profitability and effectiveness of such renewable energy projects. - Offers comprehensive coverage of wind, solar, wave, and hydropower forecasting in one convenient volume - Addresses a topic that is growing in importance, given the increasing penetration of renewable energy in many countries - Reviews state-of-the-science techniques for renewable energy forecasting - Contains chapters on operational applications

Forecasting: principles and practice
Forecasting: principles and practice

Author: Rob J Hyndman

Publisher: OTexts

Published: 2018-05-08

Total Pages: 380

ISBN-13: 0987507117

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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.

Forecasting with Exponential Smoothing
Forecasting with Exponential Smoothing

Author: Rob Hyndman

Publisher: Springer Science & Business Media

Published: 2008-06-19

Total Pages: 362

ISBN-13: 3540719180

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Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. This book brings together all of the important new results on the state space framework for exponential smoothing. It will be of interest to people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions. Part 1 provides an introduction to exponential smoothing and the underlying models. The essential details are given in Part 2, which also provide links to the most important papers in the literature. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems. Applications to particular domains are discussed in Part 4.

Introduction to Time Series and Forecasting
Introduction to Time Series and Forecasting

Author: Peter J. Brockwell

Publisher: Springer Science & Business Media

Published: 2013-03-14

Total Pages: 429

ISBN-13: 1475725264

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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.

Short-Term Load Forecasting 2019
Short-Term Load Forecasting 2019

Author: Antonio Gabaldón

Publisher: MDPI

Published: 2021-02-26

Total Pages: 324

ISBN-13: 303943442X

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Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.

Time-Series Forecasting
Time-Series Forecasting

Author: Chris Chatfield

Publisher: CRC Press

Published: 2000-10-25

Total Pages: 281

ISBN-13: 1420036203

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From the author of the bestselling "Analysis of Time Series," Time-Series Forecasting offers a comprehensive, up-to-date review of forecasting methods. It provides a summary of time-series modelling procedures, followed by a brief catalogue of many different time-series forecasting methods, ranging from ad-hoc methods through ARIMA and state-space

Introduction to Time Series Forecasting With Python
Introduction to Time Series Forecasting With Python

Author: Jason Brownlee

Publisher: Machine Learning Mastery

Published: 2017-02-16

Total Pages: 359

ISBN-13:

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Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.

Nonlinear Modeling of Economic and Financial Time-Series
Nonlinear Modeling of Economic and Financial Time-Series

Author: Fredj Jawadi

Publisher: Emerald Group Publishing

Published: 2010-12-17

Total Pages: 224

ISBN-13: 0857244892

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Presents researches in linear and nonlinear modelling of economic and financial time-series. This book provides a comprehensive understanding of financial and economic dynamics in various aspects using modern financial econometric methods. It also presents and discusses research findings and their implications.

Short-Term Forecasting for Empirical Economists
Short-Term Forecasting for Empirical Economists

Author: Maximo Camacho

Publisher:

Published: 2013-11-01

Total Pages: 74

ISBN-13: 9781601987426

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Short-term Forecasting for Empirical Economists seeks to close the gap between research and applied short-term forecasting. The authors review some of the key theoretical results and empirical findings in the recent literature on short-term forecasting, and translate these findings into economically meaningful techniques to facilitate their widespread application to compute short-term forecasts in economics, and to monitor the ongoing business cycle developments in real time.