Predictions in Time Series Using Regression Models

Predictions in Time Series Using Regression Models

Author: Cory Terrell

Publisher: Scientific e-Resources

Published: 2019-09-02

Total Pages: 300

ISBN-13: 1839473290

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Regression methods have been a necessary piece of time arrangement investigation for over a century. As of late, new advancements have made real walks in such territories as non-constant information where a direct model isn't fitting. This book acquaints the peruser with fresher improvements and more assorted regression models and methods for time arrangement examination. Open to any individual who knows about the fundamental present day ideas of factual deduction, Regression Models for Time Series Analysis gives a truly necessary examination of late measurable advancements. Essential among them is the imperative class of models known as summed up straight models (GLM) which gives, under a few conditions, a bound together regression hypothesis reasonable for constant, all out, and check information. The creators stretch out GLM methodology deliberately to time arrangement where the essential and covariate information are both arbitrary and stochastically reliant. They acquaint readers with different regression models created amid the most recent thirty years or somewhere in the vicinity and condense traditional and later outcomes concerning state space models.


Book Synopsis Predictions in Time Series Using Regression Models by : Cory Terrell

Download or read book Predictions in Time Series Using Regression Models written by Cory Terrell and published by Scientific e-Resources. This book was released on 2019-09-02 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression methods have been a necessary piece of time arrangement investigation for over a century. As of late, new advancements have made real walks in such territories as non-constant information where a direct model isn't fitting. This book acquaints the peruser with fresher improvements and more assorted regression models and methods for time arrangement examination. Open to any individual who knows about the fundamental present day ideas of factual deduction, Regression Models for Time Series Analysis gives a truly necessary examination of late measurable advancements. Essential among them is the imperative class of models known as summed up straight models (GLM) which gives, under a few conditions, a bound together regression hypothesis reasonable for constant, all out, and check information. The creators stretch out GLM methodology deliberately to time arrangement where the essential and covariate information are both arbitrary and stochastically reliant. They acquaint readers with different regression models created amid the most recent thirty years or somewhere in the vicinity and condense traditional and later outcomes concerning state space models.

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.


Book Synopsis Forecasting: principles and practice by : Rob J Hyndman

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.

Predictions in Time Series Using Regression Models

Predictions in Time Series Using Regression Models

Author: Frantisek Stulajter

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 237

ISBN-13: 1475736290

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This book will interest and assist people who are dealing with the problems of predictions of time series in higher education and research. It will greatly assist people who apply time series theory to practical problems in their work and also serve as a textbook for postgraduate students in statistics economics and related subjects.


Book Synopsis Predictions in Time Series Using Regression Models by : Frantisek Stulajter

Download or read book Predictions in Time Series Using Regression Models written by Frantisek Stulajter and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will interest and assist people who are dealing with the problems of predictions of time series in higher education and research. It will greatly assist people who apply time series theory to practical problems in their work and also serve as a textbook for postgraduate students in statistics economics and related subjects.

Applied Econometrics with R

Applied Econometrics with R

Author: Christian Kleiber

Publisher: Springer Science & Business Media

Published: 2008-12-10

Total Pages: 229

ISBN-13: 0387773185

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R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.


Book Synopsis Applied Econometrics with R by : Christian Kleiber

Download or read book Applied Econometrics with R written by Christian Kleiber and published by Springer Science & Business Media. This book was released on 2008-12-10 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

Time Series Forecasting in Python

Time Series Forecasting in Python

Author: Marco Peixeiro

Publisher: Simon and Schuster

Published: 2022-11-15

Total Pages: 454

ISBN-13: 1638351473

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Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You’ll explore interesting real-world datasets like Google’s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. About the technology You can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you’ll soon be ready to build your own accurate, insightful forecasts. What's inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the reader For data scientists familiar with Python and TensorFlow. About the author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada’s largest banks. Table of Contents PART 1 TIME WAITS FOR NO ONE 1 Understanding time series forecasting 2 A naive prediction of the future 3 Going on a random walk PART 2 FORECASTING WITH STATISTICAL MODELS 4 Modeling a moving average process 5 Modeling an autoregressive process 6 Modeling complex time series 7 Forecasting non-stationary time series 8 Accounting for seasonality 9 Adding external variables to our model 10 Forecasting multiple time series 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING 12 Introducing deep learning for time series forecasting 13 Data windowing and creating baselines for deep learning 14 Baby steps with deep learning 15 Remembering the past with LSTM 16 Filtering a time series with CNN 17 Using predictions to make more predictions 18 Capstone: Forecasting the electric power consumption of a household PART 4 AUTOMATING FORECASTING AT SCALE 19 Automating time series forecasting with Prophet 20 Capstone: Forecasting the monthly average retail price of steak in Canada 21 Going above and beyond


Book Synopsis Time Series Forecasting in Python by : Marco Peixeiro

Download or read book Time Series Forecasting in Python written by Marco Peixeiro and published by Simon and Schuster. This book was released on 2022-11-15 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You’ll explore interesting real-world datasets like Google’s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. About the technology You can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you’ll soon be ready to build your own accurate, insightful forecasts. What's inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the reader For data scientists familiar with Python and TensorFlow. About the author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada’s largest banks. Table of Contents PART 1 TIME WAITS FOR NO ONE 1 Understanding time series forecasting 2 A naive prediction of the future 3 Going on a random walk PART 2 FORECASTING WITH STATISTICAL MODELS 4 Modeling a moving average process 5 Modeling an autoregressive process 6 Modeling complex time series 7 Forecasting non-stationary time series 8 Accounting for seasonality 9 Adding external variables to our model 10 Forecasting multiple time series 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING 12 Introducing deep learning for time series forecasting 13 Data windowing and creating baselines for deep learning 14 Baby steps with deep learning 15 Remembering the past with LSTM 16 Filtering a time series with CNN 17 Using predictions to make more predictions 18 Capstone: Forecasting the electric power consumption of a household PART 4 AUTOMATING FORECASTING AT SCALE 19 Automating time series forecasting with Prophet 20 Capstone: Forecasting the monthly average retail price of steak in Canada 21 Going above and beyond

Forecasting and Time Series

Forecasting and Time Series

Author: Bruce L. Bowerman

Publisher: South Western Educational Publishing

Published: 1993

Total Pages: 746

ISBN-13:

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This comprehensive book introduces students to time series and forecasting techniques. The prerequisites are college algebra and basic statistics. It contains complete coverage of linear regression analysis, which provides much of the conceptual foundation of forecasting.


Book Synopsis Forecasting and Time Series by : Bruce L. Bowerman

Download or read book Forecasting and Time Series written by Bruce L. Bowerman and published by South Western Educational Publishing. This book was released on 1993 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive book introduces students to time series and forecasting techniques. The prerequisites are college algebra and basic statistics. It contains complete coverage of linear regression analysis, which provides much of the conceptual foundation of forecasting.

SAS for Forecasting Time Series, Third Edition

SAS for Forecasting Time Series, Third Edition

Author: John C. Brocklebank, Ph.D.

Publisher: SAS Institute

Published: 2018-03-14

Total Pages: 384

ISBN-13: 1629605441

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To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.


Book Synopsis SAS for Forecasting Time Series, Third Edition by : John C. Brocklebank, Ph.D.

Download or read book SAS for Forecasting Time Series, Third Edition written by John C. Brocklebank, Ph.D. and published by SAS Institute. This book was released on 2018-03-14 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.

Machine Learning for Time Series Forecasting with Python

Machine Learning for Time Series Forecasting with Python

Author: Francesca Lazzeri

Publisher: John Wiley & Sons

Published: 2020-12-03

Total Pages: 224

ISBN-13: 111968238X

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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.


Book Synopsis Machine Learning for Time Series Forecasting with Python by : Francesca Lazzeri

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 2020-12-03 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.

Practical Time Series Analysis

Practical Time Series Analysis

Author: Aileen Nielsen

Publisher: O'Reilly Media

Published: 2019-09-20

Total Pages: 500

ISBN-13: 1492041629

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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


Book Synopsis Practical Time Series Analysis by : Aileen Nielsen

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

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.


Book Synopsis Time Series Models for Short-Term Forecasting Performance Indicators by : Arno Palmrich

Download or read book Time Series Models for Short-Term Forecasting Performance Indicators written by Arno Palmrich and published by GRIN Verlag. This book was released on 2009-09-30 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.