Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

    címtáras azonosítással

    vissza a tantárgylistához   nyomtatható verzió    

    Trend Analysis and Visualization

    A tantárgy neve magyarul / Name of the subject in Hungarian: Trendelemzés és vizualizáció

    Last updated: 2017. június 23.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics

    Business Information Systems MSc.

    Specialization Analytical Business


    Course ID Semester Assessment Credit Tantárgyfélév
    VITMM246   3/0/1/v 5  
    3. Course coordinator and department Dr. Simon Csaba,
    Web page of the course
    4. Instructors
     Name Possitions Department
     KÓSA, Zsuzsanna Ph.Dassociate professor
    Department of Telecommunications and media-informatics
    SIMON, Csaba PhD
    assistant professorDepartment of Telecommunications and media-informatics
     PARÓCZI, Zsomborassistant research fellowDepartment of Telecommunications and media-informatics
    5. Required knowledge

    Required knowledge:

    Knowledge of statistics, finances, and business administration


    6. Pre-requisites



    7. Objectives, learning outcomes and obtained knowledge

    Objectives, learning outcomes and obtained knowledge:

    Predictive Ananlysis of time series. Mapping problems in predictive analytics, solutions in practice.   Support by  standardized tools. Show and understand the surplus of visualization, and turn it back to the data preparation and modeling phases

    8. Synopsis

    Synopsis :

    Modul_1.: Visual analytics

    Introduction to Predictive Analytics and Visualization, visual analytics

    Analytical reasoning techniques,

    Data representations and transformations

    Visual representations and interaction techniques.

    Generalized multidimensional scaling

    Perceptual mapping

    Business Decision Mapping (BDM)

    Practice in Laboratory 1: Visualization

    Modul_2: Forecasting

    a.)   Approaching a forecasting problem

    Components of a time series; Judging the quality of data; Understanding data; Looking at residuals; How to start making a forecast; Forecasting models.

    Defining parameters, Analysis of data sources; Choosing alternative projection techniques  Preliminary selection criteria


    b.)   Forecasting with exponential smoothing models

    Smoothing with moving averages; Single exponential smoothing; Compare exponential smoothing with moving averages; Exponential smoothing for trending data

    Practice in laboratory 2.  Exponential smoothing; Software programs and visualization.


    c.)   Trend and seasonality modeling and analysis;

    ANOVA model; Contribution of trend/seasonal effects;  Analysis of residuals.

    Practice in laboratory 3: Trend and seasonality; Software programs and visualization


    d.)   Preparing the data for modeling;

    Achieving linearity; Achieving normality; Dealing with outliers

    Practice in laboratory 4: Outliers; Software programs and visualization.


    e.)   Regression modeling and analysis

    Building regression models: The regression curve; A simple linear model;  The method of least-squares; Normal regression assumptions; Comparing estimation techniques;  Interpreting regression output: The R-squared statistic; The t-statistic; The F-Statistic; The D-W Statistic; Assessing forecast precision, Looking at regression residuals.

    Practice in laboratory 5: Regression example; Software programs and visualization.


    f.)    Insuring against unusual values

    The need for robustness in correlation and regression analysis Seasonal adjustment; Ratio-to-moving-average-method. Seasonal adjustment with resistant smoothers

    Practice in laboratory  6: with seasonality analysis; Software programs and visualization


    Modul_3: Foresight

    a)     Differences of foresight and forecasting

    Non measurable trend analysis: qualitative description, success factors Topic definition, starting position Ongoing projects, expected development Visualization of trends through drawing, pictures (like cicles, hype)

    b)     Visioning a usage area

    Topic definition, summary of the situation Driver analysis estimation of effects, uncertainty Scenario making, alternative scenarios, illustrations Visualization and illustration of the visions

    c)     Technology radar for foresight Flow of news, scanning news, practice for selections Professional blogging, technology radar  Virtual community to build up Games for knowledge integration

    d)     Strategy making based on backward scenarios

    Choosing objectives, freedom of choices, views Influencing drivers, costs and risks  Strategy forming through backward scenario analysis

    Practice in laboratory 7: Foresight presentations in of the students on a preliminary given topic

    Summary: Usability of predictive analysis, foresight and  visualization.

    9. Method of instruction

    Method of instruction :

    Lectures and 6 practices in laboratory

    10. Assessment


    a. In the class period there is 1  in-class test (ZH) from the topics of modul1 and modul2

                                       1 written and presented homework  from the topic modul3

    b. In the examination period: written  examination and it could be extended orally,

    c. Preliminary examination opportunity exists

    d. Condition for the signature is the pass mark of ZH test minimum 4 points from the maximum 10 points. Another condition for the signature is at least successfull attendances the laboratory exercises. One practice in laboratory can be missing.

    11. Recaps

    Recaps :

    There is one possibility to repeat the test in the teaching period. In the rectification period(repeat period) there is another (final) possibility to rewrite the in-class test (ZH).

    Only two of practices in laboratory can be repeated in an appointed time with the instructor.

    The homework presentation can be repeated int he recap period in a given data, with paying the recap fee.

    12. Consultations


    Preliminary appointing with instructors or after the lectures

    13. References, textbooks and resources

    References, textbooks and resources:

    1. Box G., Jenkins G. M., Time Series Analysis – Forecasting and Control, CA: Holden-Day, 1976.
    2. Sallehuddin, R., Shamsuddin, S. M. H., Hashim, S. Z. M., Abraham, A.: Forecasting time series data using hybrid grey relational artificial neural network and auto regressive integrated moving average model. 2007.
    3. Wong P C, Thomas J.: Visual analytics. 2004.
    4. Mark Last, Abraham Kandel, Horst Bunke: Data Mining In Time-Series Databases. World Scientific Press. 2004.
    5. Liam Fahey (Editor), Robert M. Randall (Editor): Learning from the Future: Competitive Foresight Scenarios (1997)
    14. Required learning hours and assignment
    Lessons 56
    Preparation for lessons 10
    Preparation for practices in laboratory 12
    Praparation for test  18
    Homework 4
    Preparation for exam 50
    15. Syllabus prepared by

    Syllabus prepared by:

     Name Possitions Department
     KÓSA Zsuzsanna Ph.DAssociste professor Department of Telecommunications and media-informatics