KATE GLEASON COLLEGE OF ENGINEERING. John D. Hromi Center for Quality and Applied Statistics

Transcription

1 ROCHESTER INSTITUTE OF TECHNOLOGY COURSE OUTLINE FORM KATE GLEASON COLLEGE OF ENGINEERING John D. Hromi Center for Quality and Applied Statistics NEW (or REVISED) COURSE (KCOE-CQAS Time Series Analysis and Forecasting): 1.0 Course Designations and Approvals Required course approvals: Academic Unit Curriculum Committee College Curriculum Committee Optional designations: Is designation desired? General Education: Yes No Writing Intensive: Yes No Honors Yes No Approval request date: *Approval request date: Approval granted date: **Approval granted date: 2.0 Course information: Course title: KCOE-CQAS Time Series Analysis and Forecasting Credit hours: 3 Prerequisite(s): KCOE-CQAS- 741 Regression Analysis Co-requisite(s): None Course proposed by: Ernest Fokoué Effective date: August 2013 Contact hours Maximum students/section Classroom 3 25 Lab 0 KGCOE-CQAS-741 Regression Analysis Studio 0 Other (specify) 0 2.a Course Conversion Designation*** (Please check which applies to this course). *For more information on Course Conversion Designations please see page four. Semester Equivalent (SE) Please indicate which quarter course it is equivalent to: Time Series Analysis and Forecasting New September 2010

2 2.b Semester(s) offered (check) Fall (distance) Spring (campus) Summer Other All courses must be offered at least once every 2 years. If course will be offered on a bi-annual basis, please indicate here: 2.c Student Requirements Students required to take this course: (by program and year, as appropriate) Elective Students who might elect to take the course: Any MS Applied Statistics Students or any other RIT graduate students In the sections that follow, please use sub-numbering as appropriate (eg. 3.1, 3.2, etc.) 3.0 Goals of the course (including rationale for the course, when appropriate): 3.1 Understand the fundamental and crucial difference between time series data and traditional independent and identically distributed samples used in basic statistics 3.2 Learn and master the basic concepts and methods of time series analysis and forecasting to the point of applying them to many real world case studies 3.3 Use a statistical software (Minitab, SAS or R) to perform a complete time series analysis and forecasting on a real world case study and write a complete report of the findings along with well supported recommendations 4.0 Course description (as it will appear in the RIT Catalog, including pre- and corequisites, and quarters offered). Please use the following format: Course: KGCOE-CQAS-873 Time Series Analysis and Forecasting This course is designed to provide the student with a solid practical hands-on introduction to the fundamentals of time series analysis and forecasting. Topics include stationarity, filtering, differencing, time series decomposition, time series regression, exponential smoothing, and Box-Jenkins techniques. Within each of these we will discuss seasonal and nonseasonal models. Many real-world examples will be covered and demonstrated using modern statistical software.this course is designed to provide the student with a solid practical hands-on introduction to the fundamentals of time series analysis and forecasting, with a strong emphasis on the wide applicability of these techniques to real world problems. Throughout the course, many real world case studies are used to motivate and explain the strengths and appropriateness of each method of interest. Examples are chosen from such fields as financial investment, sales forecasting, weather forecasting, just to name a few. Among other topics, we ll cover: statistical and probabilistic tools for time series analysis and forecasting, the importance of stationarity, filtering, differencing, time series decomposition, stationary time series models, moving average models, autoregressive models, autoregressive moving average (ARMA) models, ARIMA models, model selection and parameter estimation in time series analysis, Box-Jenkins approach to time series analysis and modeling, The statistical software environment R provides an excellent collection of built-in functions and contributed packages that make the 2

3 exploration of time series concepts both straightforward and very practical. Prerequisite(s): KGCOE-CQAS-741 Regression Analysis Class 3, Lab 0, Credit 3 (Fall-distance, Spring-campus) 3

4 5.0 Possible resources (texts, references, computer packages, etc.) Required texts: 5.1 Introduction to Time Series Analysis and Forecasting, By Douglas C. Montgomery, Cheryl L. Jennings, and Murat Kulahci, (2008), John Wiley & Sons, Inc 6.0 Topics (outline): 1. Introduction to Forecasting. 1.1 The Nature and uses of Forecasts. 1.2 Some Examples of Time Series. 1.3 The Forecasting Process. 1.4 Resources for Forecasting. 2. Statistics Background for Forecasting. 2.2 Graphical Displays. 2.3 Numerical Description of Time Series Data. 2.4 Use of Data Transformations and Adjustments. 2.5 General Approach to Time Series Analysis and Forecasting. 2.6 Evaluating and Monitoring Forecasting Model Performance. 3. Regression Analysis and Forecasting. 3.2 Least Squares Estimation in Linear Regression Models. 3.3 Statistical Inference in Linear Regression. 3.4 Prediction of New Observations. 3.5 Model Adequacy Checking. 3.6 Variable Selection Methods in Regression. 3.7 Generalized and Weighted Least Squares. 3.8 Regression Models for General Time Series Data. 4. Exponential Smoothing Methods. 4.2 First-Order Exponential Smoothing. 4.3 Modeling Time series Data. 4.4 Second-Order Exponential Smoothing. 4.5 Higher-Order Exponential Smoothing. 4.6 Forecasting. 4.7 Exponential Smoothing for Seasonal Data. 4.8 Exponential Smoothers and ARIMA Models. 5. Autoregressive Integrated Moving Average (ARIMA) Models. 5.1 Introduction. 5.2 Linear Models for Stationary Time Series. 5.3 Finite Order Moving Average (MA) Processes. 5.4 Finite Order Autoregressive Processes. 5.5 Mixed Autoregressive-Moving Average (ARMA) Processes. 5.6 Non-stationary Processes. 5.7 Time Series Model Building. 5.8 Forecasting ARIMA Processes. 5.9 Seasonal Processes.. 4

5 7.0 Intended course learning outcomes and associated assessment methods of those outcomes (please include as many Course Learning Outcomes as appropriate, one outcome and assessment method per row). Course Objectives Level 2: Comprehension: 2.1.Understand the fundamental and crucial difference between time series data and traditional independent and identically distributed samples used in basic statistics. 2.2.Gains a deep understanding of stationarity and its importance in the analysis of time series 2.3.Appreciates the power of statistical concepts and tools like confidence intervals and hypothesis tests to quantify the inevitable uncertainty and variation inherent in real world problems Level 3: Application: 3.1.Identifies an interesting real world problem during the course of study and formulates its statistically 3.2.Collects good quality data from a variety of sources (Internet, sampling in the engineering lab, etc) 3.3.Uses statistical software to perform an informal analysis of the data through a thorough exploratory statistical data analysis, with the finality of gaining insights into some plausible models that could capture the patterns underlying the data Level 4: Analysis: 4.1.Determines/decides which statistical model(s) appear to be most appropriate for the task at hand in light of the graphs and descriptive statistics obtained. 4.2.Fits the chosen plausible model(s) using a statistical software package like Minitab, R or SAS, then extracts and interprets the estimates of the parameters 4.3.Performs additional statistical hypothesis tests wherever needed 4.4.Checks all the assumptions underlying each method/technique used 4.5.Interprets the statistical estimation and prediction results produced by the software package Assessment Method Homework Exams Projects 5

6 Level 5: Synthesis: 5.1.Selects the best model according to some of the usual model selection criteria 5.2.Provides any needed/required formal prediction/forecasting or estimation. 5.3.Draws conclusions and interpretations about the original engineering task based on sound formal analysis like confidence intervals and results of hypothesis testing. Level 6: Evaluation: 6.1.Evaluates several potential statistical models and decides on the most appropriate one for the given purpose. 6.2.Provides any needed/required formal prediction/forecasting or estimation 6.3.Makes recommendations in clear and non technical language based a thorough assessment of the statistical findings 8.0 Program outcomes and/or goals supported by this course Relationship to Program Outcomes (1 = slightly, 2=moderately, 3=significantly) Program Outcomes and/or Goals for CQAS 8.1 Advanced Certificate in Lean Six Sigma Demonstrates an solid understanding of statistical thinking and Lean Six Sigma methodology in solving real-world problems Leads Lean Six Sigma improvement projects. Level of Support Advanced Certificate and Masters of Science in Applied Statistics Demonstrates solid understanding of statistical thinking and applied statistics methodology in solving real-world problems Designs studies that are efficient and valid Analyzes data using appropriate statistical methods Communicates the results of statistical analysis with effective reports and presentations. Note: Students obtaining the Advanced Certificate in Applied Statistics will not be expected to perform at the same level as students obtaining a Master of Science degree. 6

7 9.0 - Not Applicable General Education Learning Outcome Supported by the Course, if appropriate Communication Express themselves effectively in common college-level written forms using standard American English Revise and improve written and visual content Express themselves effectively in presentations, either in spoken standard American English or sign language (American Sign Language or English-based Signing) Comprehend information accessed through reading and discussion Intellectual Inquiry Review, assess, and draw conclusions about hypotheses and theories Analyze arguments, in relation to their premises, assumptions, contexts, and conclusions Construct logical and reasonable arguments that include anticipation of counterarguments Use relevant evidence gathered through accepted scholarly methods and properly acknowledge sources of information Ethical, Social and Global Awareness Analyze similarities and differences in human experiences and consequent perspectives Examine connections among the world s populations Identify contemporary ethical questions and relevant stakeholder positions Scientific, Mathematical and Technological Literacy Explain basic principles and concepts of one of the natural sciences Apply methods of scientific inquiry and problem solving to contemporary issues Comprehend and evaluate mathematical and statistical information Perform college-level mathematical operations on quantitative data Describe the potential and the limitations of technology Use appropriate technology to achieve desired outcomes Creativity, Innovation and Artistic Literacy Demonstrate creative/innovative approaches to course-based assignments or projects Interpret and evaluate artistic expression considering the cultural context in which it was created Assessment Method 10.0 Other relevant information (such as special classroom, studio, or lab needs, special scheduling, media requirements, etc.) None 7

8 *Optional course designation; approval request date: This is the date that the college curriculum committee forwards this course to the appropriate optional course designation curriculum committee for review. The chair of the college curriculum committee is responsible to fill in this date. **Optional course designation; approval granted date: This is the date the optional course designation curriculum committee approves a course for the requested optional course designation. The chair of the appropriate optional course designation curriculum committee is responsible to fill in this date. ***Course Conversion Designations Please use the following definitions to complete table 2.a on page one. Semester Equivalent (SE) Closely corresponds to an existing quarter course (e.g., a 4 quarter credit hour (qch) course which becomes a 3 semester credit hour (sch) course.) The semester course may develop material in greater depth or length. Semester Replacement (SR) A semester course (or courses) taking the place of a previous quarter course(s) by rearranging or combining material from a previous quarter course(s) (e.g. a two semester sequence that replaces a three quarter sequence). New (N) - No corresponding quarter course(s). 8