Browsing by Subject "Time Series"

Now showing 1 - 4 of 4

Open Access
A comparative performance analysis for the commonly used time series filters in economics : Hodrick-Prescott versus Baxter-King
(2001) Yüksel, Ebru
This thesis compares the performance of the Hodrick-Prescott filter commonly employed in economic analysis to separate the trend of a given non-stationary time series from its cyclical components, to that of the Band-Pass filter developed by Baxter and King. The performances of detrending techniques under consideration are evaluated by constructing special time series that mimic the pattern of actually observed series of interest using synthesized cyclical and trend components. As an illustration of the use of this approach, the behavior of the ISE-100 index of Istanbul Stock Exchange and the Jasdaq index of Japanese Stock Market are analyzed.
Open Access
Comparison of the forecast performances of linear time series and artificial neural network models within the context of Turkish inflation
(2001) Uçar, Nuri
This thesis compares a variety of linear and nonlinear models to find the one with the best inflation forecast performance for the Turkish Economy. These comparisons are performed by considering the type of series whether or not stationary. Different combination techniques are applied to improve the forecasts. It is observed that the combination forecasts based on nonstationary vector autoregressive (VAR) and artificial neural network (ANN) models are better than the ones generated by other models. Furthermore, the forecast values combined with ANN technique produce lower root mean square errors (RMSE) than the other combination techniques.
Open Access
Online minimax optimal density estimation and anomaly detection in nonstationary environments
(2017-07) Gökcesu, Kaan
Online anomaly detection has attracted signi cant attention in recent years due to its applications in network monitoring, cybersecurity, surveillance and sensor failure. To this end, we introduce an algorithm that sequentially processes data to detect anomalies in time series. Our algorithm consists of two stages: density estimation and anomaly detection. First, we construct a probability density function to model the normal data. Then, we threshold the density of the newly observed data to detect anomalies. We approach this problem from an information theoretic perspective and, for the rst time in the literature, propose minimax optimal schemes for both stages to create an optimal anomaly detection algorithm in a strong deterministic sense. For the rst stage, we introduce an online density estimator that is minimax optimal for general nonstationary exponential-family of distributions without any assumptions on the observation sequence. Our algorithm does not require a priori knowledge of the time horizon, the drift of the underlying distribution or the time instances the parameters of the source changes. Our results are guaranteed to hold in an individual sequence manner. For the second stage, we propose an online threshold selection scheme that has logarithmic performance bounds against the best threshold chosen in hindsight. Our complete algorithm adaptively updates its parameters in a truly sequential manner to achieve log-linear regrets in both stages. Because of its universal prediction perspective on its density estimation, our anomaly detection algorithm can be used in unsupervised, semi-supervised and supervised manner. Through synthetic and real life experiments, we demonstrate substantial performance gains with respect to the state-of-the-art.
Open Access
Relevance feedback and sparsity handling methods for temporal data
(2018-07) Eravcı, Bahaeddin
Data with temporal ordering arises in many natural and digital processes with an increasing importance and immense number of applications. This study provides solutions to data mining problems in analyzing time series both in standalone and sparse networked cases. We initially develop a methodology for browsing time series repositories by forming new time series queries based on user annotations. The result set for each query is formed using diverse selection methods to increase the effectiveness of the relevance feedback (RF) mechanism. In addition to RF, a unique aspect of time series data is considered and representation feedback methods are proposed to converge to the outperforming representation type among various transformations based on user annotations as opposed to manual selection. These methods are based on partitioning of the result set according to representation performance and a weighting approach which amplifies different features from multiple representations. We subsequently propose the utilization of autoencoders to summarize the time series into a data-aware sparse representation to both decrease computation load and increase the accuracy. Experiments on a large variety of real data sets prove that the proposed methods improve the accuracy significantly and data-aware representations have recorded similar performances while reducing the data and computational load. As a more demanding case, the time series dataset may be incomplete needing interpolation approaches to apply data mining techniques. In this regard, we analyze a sparse time series data with an underlying time varying network. We develop a methodology to generate a road network time series dataset using noisy and sparse vehicle trajectories and evaluate the result using time varying shortest path solutions.