Time Series Models for Short-Term Forecasting Performance Indicators

Table of Contents

1 Introduction

1.1 Outline of the Historical Background of Forecasting

1.2 Motivation

1.3 Methodology

2 Review of Literature

3 Description of Data

4 Analysis

4.1 Building a Model for Forecasting Cost of Goods Sold

4.2 Building a Model for Forecasting Net Sales

4.3 Computation

4.4 Assumptions of the Classical Linear Regression Model

4.5 Validation of Assumptions

4.5.1 Assumption 1

4.5.2 Assumption 2

4.5.3 Assumption 3

4.5.4 Assumption 4

4.5.5 Assumption 5

4.5.6 Assumption 6

4.5.7 Assumption 7

4.6 Weighted Least Squares Regression

4.7 Time Series Analysis

4.7.1 The Autoregressive Process

4.7.2 The Moving Average Process

4.7.3 The Autoregressive Moving Average Process

4.7.4 The Autoregressive Integrated Moving Average Model

4.7.5 Model Identification

4.7.6 Model Estimation

4.7.7 Diagnosis

5 Conclusion

5.1 Forecasting

5.2 Outlook

Research Objectives and Key Topics

The primary research objective is to develop and evaluate statistical forecasting tools using existing company data to predict short-term key performance indicators—specifically net sales, Cost of Goods Sold (COGS), and net contribution—for Dalian Chemson Chemical Products Co; Ltd. (DCCP). The thesis aims to replace reliance on mere assumptions with evidence-based mathematical models to improve budget accuracy and management decision-making.

• Application of linear regression and advanced time series analysis in a corporate environment.
• Evaluation of Ordinary Least Squares (OLS) versus Weighted Least Squares (WLS) regression models.
• Implementation of Autoregressive Integrated Moving Average (ARIMA) models for short-term prediction.
• Diagnostic testing for model validity (linearity, heteroscedasticity, and autocorrelation).
• Development of practical forecasting frameworks for implementation in spreadsheet software.

Excerpt from the Book

Summary of Chapters

1 Introduction: Provides historical context on forecasting theory and defines the motivation and methodology for improving DCCP's budgeting through statistical modeling.

2 Review of Literature: Surveys standard statistical textbooks and recent advancements in regression and time series analysis relevant to the study.

3 Description of Data: Details the quantitative variables and longitudinal data collected from internal company reports for the purpose of empirical modeling.

4 Analysis: Documents the construction, validation, and computational testing of various regression and time series models (OLS, WLS, ARIMA) to predict COGS and net sales.

5 Conclusion: Evaluates the performance of the developed models against actual P&L figures and discusses the practical application for future forecasting.

Keywords

Forecasting, Time Series Analysis, Linear Regression, Cost of Goods Sold, Net Sales, ARIMA, Ordinary Least Squares, Weighted Least Squares, Heteroscedasticity, Autocorrelation, Stationarity, Statistical Modeling, Budgeting, Performance Indicators, Dalian Chemson Chemical Products

Frequently Asked Questions

What is the fundamental purpose of this research?

The study aims to provide the management of Dalian Chemson Chemical Products Co; Ltd. with a robust, evidence-based method to generate short-term forecasts for key financial metrics, moving away from subjective assumptions.

What are the primary thematic areas covered in this work?

The paper focuses on the intersection of industrial financial reporting and quantitative statistical methods, specifically emphasizing regression analysis and time series modeling.

What is the central research question?

The research asks how existing company data can be leveraged via statistical models to make accurate, short-term predictions for net sales, COGS, and net contribution.

Which scientific methods are employed throughout the analysis?

The author utilizes Ordinary Least Squares (OLS) regression, Weighted Least Squares (WLS) regression to address heteroscedasticity, and Box-Jenkins ARIMA time series modeling to account for temporal dependencies.

What content is addressed in the analytical main section?

The analysis covers the derivation of forecasting equations, the rigorous validation of model assumptions (such as linearity and normality of residuals), and the comparative evaluation of different model versions.

Which keywords characterize this thesis?

Key terms include forecasting, time series analysis, regression modeling, COGS, net sales, and statistical model identification.

How is the impact of raw material costs integrated into the model?

The author identifies the market price of lead as a critical regressor and uses lagged values of this price to anticipate changes in the cost of lead-containing raw materials, which significantly impact COGS.

Why did the author conclude that OLS and WLS models perform adequately?

The author observed that the predicted values closely track the actual data series, suggesting that despite simplifications, these models provide sufficient predictive utility for management decision-making.