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Courses in summer term 2007 / Seminar on Econometrics and Time Series Analysis / readings:
abstract
readings

Note on Seminar Paper :

  • Due four weeks after end of term (Monday, 13.08.07),
  • 30 pages at most,
  • 3 hard copies(printed) and 1 soft copy (CD).
    • Note on Grades:
  • Marks will be based on presentation (including answers to questions), seminar paper, and general participation (e.g., asking questions),
  • bonuses for own experiments or implementations. If any, please also include in the CD.

    • List of readings (ee = link to electronic edition; ask me for the other references):

    1. -- Introduction --
    2. Introduction to Time Series and ARMA models.
      1. P. J. Brockwell , R. A. Davis (2002): ARMA Models Introduction to Time Series and Forecasting. , Springer , pp. 83-108.
      2. James D. Hamilton (2002): Stationary ARMA Processe. Time Series Analysis. , Princeton University Press , pp. 43-61.
    3. Wed. 06.06. Time series analysis with machine learning methods. Speaker: David Crowder
      1. [ee] M. Harries, K. Horn (1995): Detecting concept drift in financial time series prediction using symbolic machine learning Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence, pp. 91--98.
      2. [ee] Stefan Rüping, Katharina Morik (2003): Support Vector Machines And Learning About Time IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 864-7.
    4. Wed. 06.06. Pattern Detection in Time Series. Speaker: André Busche
      1. [ee] Jessica Lin, Eamonn Keogh, Stefano Lonardi, Pranav Patel (2002): Finding Motifs in Time Series Proceedings of the Second Workshop on Temporal Data Mining (KDD).
      2. [ee] Ajumobi Udechukwu , Ken Barker , Reda Alhajj (2004): An Efficient Framework For Iterative Time-Series Trend Mining ICEIS 2004: Artificial Intelligence and Decision Support Systems, pp. 130-137.
    5. Clustering and Similarity Measures for Time Series.
      1. [ee] Konstantinos Kalpakis , Dhiral Gada , Vasundhara Puttagunta (2001): Distance Measures for Effective Clustering of ARIMA Time-Series Proceedings of the international conference on data mining (ICDM), pp. 273-280.
      2. [ee] S. Focardi (2001): Clustering economic and financial time series: exploring the existence of stable correlation conditions Technical Report 2001-04, The Intertek Group.

        3. Further Reading (optionally):
      3. [ee] Jessica Lin , Michail Vlachos , Eamonn Keogh , Dimitrios Gunopulos (2004): Iterative Incremental Clustering of Time Series EDBT .
    6. Wed. 13.06. Indexing Time Series for Search. Speaker: Stefan Salzmann
      1. [ee] E. Keogh (2002): Exact indexing of dynamic time warping Proceedings of VLDB 2002.
      2. [ee] T.C. Fu, F.L. Chung, R. Luk , C.M. Ng (2004): Financial Time Series Indexing Based on Low Resolution Clustering ICDM 2004 Temporal Data Mining Workshop, pp. 5-14.
    7. Wed. 20.06. Time Series Classification. Speaker: Uwe Dobbratz
      1. [ee] Kadous Mohammed Waleed , C. Sammut (2004): Constructive induction for classifying time series Proceedings of the 15th European Conference on Machine Learning (ECML'04) , pp. 192-204 .
      2. [ee] Yuu Yamada , Einoshin Suzuki , Hideto Yokoi , Katsuhiko Takabayashi (2003): Decision-tree Induction from Time-series Data Based on a Standard-example Split Test Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003) .
    8. Wed. 27.06. Semi-Supervised Time Series Classification. Speaker: Kristin Behrens
      1. [ee] Li Wei , Eamonn Keogh (2006): Semi-Supervised Time Series Classification Proceedings of the Knowledge Discovery and Data Mining 2006, pp. 748-753.
    9. Prediction of Financial Time Series unsing nearest neighbor classification.
      1. [ee] M. Maggini, C.L. Giles, B. Horne (1997): Financial Time Series Forecasting Using K-Nearest Neighbors Classification In Proceedings of Nonlinear Financial Forecasting, pp. 169-181.
      2. [ee] John Barkoulas, Christopher F. Baum, Atreya Chakraborty (2003): Nearest-Neighbor Forecasts of U.S. Interest Rates International Journal of Banking and Finance, pp. 119-135.
    10. Wed. 11.07. Forcasting with Neuronal Networks. Speaker: Carsten Witzke
      1. [ee] Benjamin W. Wah , Minglun Qian (2001): Violation-Guided Neural-Network Learning For Constrained Formulations In Time-Series Predictions International Journal of Computational Intelligence and Applications Vol. 1 Nr. 4 .
      2. [ee] A. Lendasse , E. De Bodt , V. Wertz , M. Verleysen (2000): Non-linear financial time series forecasting – Application to the Bel 20 stock market index European Journal of Economic and Social Systems 14 N° 1 , pp. 81-91.

        3. Further Reading (optionally):
      3. [ee] John Moody (1995): Economic Forecasting: Challenges and Neural Network Solutions Proceedings of the International Symposium on Artificial Neural Networks.
    11. Outlier Detection in Time Series
      1. [ee] Zakia Ferdousi , Akira Maeda (2006): Unsupervised Outlier Detection in Time Series Data ICDE Workshops 2006 .