Now showing items 1-10 of 10

    • Diversity based Relevance Feedback for Time Series Search 

      Eravci, B.; Ferhatosmanoglu H. (2013)
      We propose a diversity based relevance feedback approach for time series data to improve the accuracy of search results. We first develop the concept of relevance feedback for time series based on dual-tree complex wavelet ...
    • Efficiency of the Turkish stock exchange with respect to monetary variables: a cointegration analysis 

      Muradoglu, Y. G.; Metin, K. (Elsevier BV, 1996)
      In this study, we test the semistrong form of the efficient market hypothesis in Turkey by using the recently developed techniques in time series econometrics, namely unit roots and cointegration. The long run relationship ...
    • Environment Kuznets curve for CO2 emissions: a cointegration analysis for China 

      Jalil, A.; Mahmud, S. F. (Elsevier Ltd, 2009)
      This study examines the long-run relationship between carbon emissions and energy consumption, income and foreign trade in the case of China by employing time series data of 1975-2005. In particular the study aims at testing ...
    • Essays on financial development and economic growth 

      Şendeniz Yüncü, İlkay (Bilkent University, 2007)
      The relationship between nancial development and economic growth is analyzed in this dissertation. The rst essay investigates the roles of banking sector development and stock market development in economic growth and ...
    • Online anomaly detection under Markov statistics with controllable type-I error 

      Ozkan, H.; Ozkan, F.; Kozat, S. S. (Institute of Electrical and Electronics Engineers Inc., 2016)
      We study anomaly detection for fast streaming temporal data with real time Type-I error, i.e., false alarm rate, controllability; and propose a computationally highly efficient online algorithm, which closely achieves a ...
    • Online anomaly detection with minimax optimal density estimation in nonstationary environments 

      Gokcesu, K.; Kozat, S. S. (Institute of Electrical and Electronics Engineers, 2018)
      We introduce a truly online anomaly detection algorithm that sequentially processes data to detect anomalies in time series. In anomaly detection, while the anomalous data are arbitrary, the normal data have similarities ...
    • Online learning under adverse settings 

      Özkan, Hüseyin (Bilkent University, 2015-05)
      We present novel solutions for contemporary real life applications that generate data at unforeseen rates in unpredictable forms including non-stationarity, corruptions, missing/mixed attributes and high dimensionality. ...
    • Online nonlinear modeling for big data applications 

      Khan, Farhan (Bilkent University, 2017-12)
      We investigate online nonlinear learning for several real life, adaptive signal processing and machine learning applications involving big data, and introduce algorithms that are both e cient and e ective. We present ...
    • Scaling forecasting algorithms using clustered modeling 

      Gür, İ.; Güvercin, M.; Ferhatosmanoglu, H. (Association for Computing Machinery, 2015)
      Research on forecasting has traditionally focused on building more accurate statistical models for a given time series. The models are mostly applied to limited data due to efficiency and scalability problems. However, ...
    • Wavelet energy ratio unit root tests 

      Trokić, M. (Taylor and Francis Inc., 2016)
      This article uses wavelet theory to propose a frequency domain nonparametric and tuning parameter-free family of unit root tests. The proposed test exploits the wavelet power spectrum of the observed series and its fractional ...