1,238 research outputs found
Time series analysis using fractal theory and online ensemble classifiers with application to stock portfolio optimization
Neural Network method is a technique that is heavily researched and used in applications
within the engineering field for various purposes ranging from process
control to biomedical applications. The success of Neural Networks (NN) in engineering
applications, e.g. object tracking and face recognition has motivated its
application to the finance industry. In the financial industry, time series data is
used to model economic variables. As a result, finance researchers, portfolio managers
and stockbrokers have taken interest in applying NN to model non-linear
problems they face in their practice. NN facilitates the approach of predicting
stocks due to its ability to accurately and intuitively learn complex patterns and
characterizes these patterns as simple equations. In this research, a methodology
that uses fractal theory and NN framework to model the stock market behavior
is proposed and developed. The time series analysis is carried out using the
proposed approach with application to modelling the Dow Jones Average Index’s
future directional movement. A methodology to establish self-similarity of time
series and long memory effects that result in classifying the time series signal as
persistent, random or non-persistent using the rescaled range analysis technique is
developed. A linear regression technique is used for the estimation of the required
parameters and an incremental online NN algorithm is implemented to predict
the directional movement of the stock. An iterative fractal analysis technique is
used to select the required signal intervals using the approximated parameters.
The selected data is later combined to form a signal of interest and then pass it
to the ensemble of classifiers. The classifiers are modelled using a neural network
based algorithm. The performance of the final algorithm is measured based on
accuracy of predicting the direction of movement and also on the algorithm’s
confidence in its decision-making. The improvement within the final algorithm
is easily assessed by comparing results from two different models in which the
first model is implemented without fractal analysis and the second model is implemented
with the aid of a strong fractal analysis technique. The results of the
first NN model were published in the Lecture Notes in Computer Science 2006
by Springer. The second NN model incorporated a fractal theory technique.
The results from this model shows a great deal of improvement when classifying
the next day’s stock direction of movement. A summary of these results were
submitted to the Australian Joint Conference on Artificial Intelligence 2006 for
publishing. Limitations on the sample size, including problems encountered with
the proposed approach are also outlined in the next sections. This document also
outlines recommendations that can be implemented as further steps to advance
and improve the proposed approach for future work
Time series classification based on fractal properties
The article considers classification task of fractal time series by the meta
algorithms based on decision trees. Binomial multiplicative stochastic cascades
are used as input time series. Comparative analysis of the classification
approaches based on different features is carried out. The results indicate the
advantage of the machine learning methods over the traditional estimating the
degree of self-similarity.Comment: 4 pages, 2 figures, 3 equations, 1 tabl
Fractional norms and quasinorms do not help to overcome the curse of dimensionality
The curse of dimensionality causes the well-known and widely discussed
problems for machine learning methods. There is a hypothesis that using of the
Manhattan distance and even fractional quasinorms lp (for p less than 1) can
help to overcome the curse of dimensionality in classification problems. In
this study, we systematically test this hypothesis. We confirm that fractional
quasinorms have a greater relative contrast or coefficient of variation than
the Euclidean norm l2, but we also demonstrate that the distance concentration
shows qualitatively the same behaviour for all tested norms and quasinorms and
the difference between them decays as dimension tends to infinity. Estimation
of classification quality for kNN based on different norms and quasinorms shows
that a greater relative contrast does not mean better classifier performance
and the worst performance for different databases was shown by different norms
(quasinorms). A systematic comparison shows that the difference of the
performance of kNN based on lp for p=2, 1, and 0.5 is statistically
insignificant
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels
Towards the text compression based feature extraction in high impedance fault detection
High impedance faults of medium voltage overhead lines with covered conductors can be identified by the presence of partial discharges. Despite it is a subject of research for more than 60 years, online partial discharges detection is always a challenge, especially in environment with heavy background noise. In this paper, a new approach for partial discharge pattern recognition is presented. All results were obtained on data, acquired from real 22 kV medium voltage overhead power line with covered conductors. The proposed method is based on a text compression algorithm and it serves as a signal similarity estimation, applied for the first time on partial discharge pattern. Its relevancy is examined by three different variations of classification model. The improvement gained on an already deployed model proves its quality.Web of Science1211art. no. 214
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