135,044 research outputs found
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short) has
emerged as a desirable property of deep neural networks. Intuitively,
robustness means that small perturbations to an input do not cause the network
to perform misclassifications. In this paper, we present a novel algorithm for
verifying robustness properties of neural networks. Our method synergistically
combines gradient-based optimization methods for counterexample search with
abstraction-based proof search to obtain a sound and ({\delta}-)complete
decision procedure. Our method also employs a data-driven approach to learn a
verification policy that guides abstract interpretation during proof search. We
have implemented the proposed approach in a tool called Charon and
experimentally evaluated it on hundreds of benchmarks. Our experiments show
that the proposed approach significantly outperforms three state-of-the-art
tools, namely AI^2 , Reluplex, and Reluval
Finding kernel function for stock market prediction with support vector regression
Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. Time series forcasting, Neural Network (NN) and Support Vector Machine (SVM) are once commonly used for prediction on stock price. In this study, the data mining operation called time series forecasting is implemented. The large amount of stock data collected from Kuala Lumpur Stock Exchange is used for the experiment to test the validity of SVMs regression. SVM is a new machine learning technique with principle of structural minimization risk, which have greater generalization ability and proved success in time series prediction. Two kernel functions namely Radial Basis Function and polynomial are compared for finding the accurate prediction values. Besides that, backpropagation neural network are also used to compare the predictions performance. Several experiments are conducted and some analyses on the experimental results are done. The results show that SVM with polynomial kernels provide a promising alternative tool in KLSE stock market prediction
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field
Training Process Reduction Based On Potential Weights Linear Analysis To Accelarate Back Propagation Network
Learning is the important property of Back Propagation Network (BPN) and
finding the suitable weights and thresholds during training in order to improve
training time as well as achieve high accuracy. Currently, data pre-processing
such as dimension reduction input values and pre-training are the contributing
factors in developing efficient techniques for reducing training time with high
accuracy and initialization of the weights is the important issue which is
random and creates paradox, and leads to low accuracy with high training time.
One good data preprocessing technique for accelerating BPN classification is
dimension reduction technique but it has problem of missing data. In this
paper, we study current pre-training techniques and new preprocessing technique
called Potential Weight Linear Analysis (PWLA) which combines normalization,
dimension reduction input values and pre-training. In PWLA, the first data
preprocessing is performed for generating normalized input values and then
applying them by pre-training technique in order to obtain the potential
weights. After these phases, dimension of input values matrix will be reduced
by using real potential weights. For experiment results XOR problem and three
datasets, which are SPECT Heart, SPECTF Heart and Liver disorders (BUPA) will
be evaluated. Our results, however, will show that the new technique of PWLA
will change BPN to new Supervised Multi Layer Feed Forward Neural Network
(SMFFNN) model with high accuracy in one epoch without training cycle. Also
PWLA will be able to have power of non linear supervised and unsupervised
dimension reduction property for applying by other supervised multi layer feed
forward neural network model in future work.Comment: 11 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS 2009, ISSN 1947 5500, Impact factor 0.42
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