Approximating sliding windows by cyclic tree-like histograms for efficient range queries

Buccafurri, Francesco; Lax, Gianluca

doi:10.1016/j.datak.2010.05.002

Cited by 9 publications

(3 citation statements)

References 58 publications

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“…In streaming data, especially stock data, one cannot assume that the past determinations of the discretization parameters will remain the same in the future since the distribution of the underlying data may change (see Section 4.2 on Concept Drift). One solution for this is to discretize over fixed and sliding time windows batch learning classifiers [32,33,66,84,204]. The idea is that when the underlying concept remains stable, the size of the window increases to allow for more data; when the concept drifts, the window shrinks.…”

Section: Discretizationmentioning

confidence: 99%

Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction

Rechenthin¹

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This thesis explores predictability in the market and then designs a decision support framework that can be used by traders to provide suggested indications of future stock price direction along with an associated probability of making that move.Markets do not remain stable and approaches that are highly predictive at one moment may cease to be so as more traders spot the patterns and adjust their trading techniques. Ideally, if these "concept drifts" could be anticipated, then the trader could store models to use with each specific market condition (or concept) and later apply those models to incoming data. The assumption however is that the future is uncertain, therefore future concepts are still undecided.Maintaining a model with only the most up-to-date price data is not necessarily the most ideal choice since the market may stabilize and old knowledge may become useful again. Additionally, decreasing training times to enable modified classifiers to work with streaming high-frequency stock data may result in decreases in performance (e.g. accuracy or AUC) due to insufficient learning times. Our framework takes a different approach to learning with drifting concepts, which is to assume that concept drift occurs and builds this into the model. The framework adapts to these market changes by building thousands of traditional base classifiers (SVMs, Decision Trees, and Neural Networks), using random subsets of past data, and covering similar (sector) stocks and heuristically combining the best of these base classifiers.This "ensemble", or pool of multiple models selected to achieve better predictive periii formance, is then used to predict future market direction. As the market moves, the base classifiers in the ensemble adapt to stay relevant and keep a high level of model performance. Our approach outperforms existing published algorithms.This thesis also addresses problems specific to learning with stock data streams, specifically class imbalance, attribute creation (e.g. technical and sentiment analysis), dimensionality reduction, and model performance due to release of news and time of

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Section: Discretizationmentioning

confidence: 99%

Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction

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“…The data being mined are now often in the form of streaming data [3,35,6], and an important problem in this area is that of detecting frequent items in a data stream. The problem of frequent item discovery in streaming data has attracted much attention, because it is relevant to many different applications across various domains [18,20,17].…”

Section: Introductionmentioning

confidence: 99%

Identifying streaming frequent items in ad hoc time windows

Dallachiesa

Palpanas

2013

Data & Knowledge Engineering

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“…In addition, the histogram approach shows a good capability of data extraction in order to get accurate values based on capacity and PSNR. 6,27 More specifically, 31 based on our experimental works, the performance of the DWT and DCT techniques are compared and observed by maintaining a good PSNR and embedding payload for the MR images. The histogram shifting 1 achieves good strategy and high quality of the stego image.…”

Section: Introductionmentioning

confidence: 99%

A novel embedding technique for lossless data hiding in medical images employing histogram shifting method

Balan

Rajan²

2014

Int. J. Wavelets Multiresolut Inf. Process.

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In this paper, a lossless data hiding method based on histogram shifting for MR images using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are presented. In this method, the algorithms are validated to hide the data in wavelet coefficients of high frequency subbands. This scheme has the advantage of comparing the DCT coefficients and the DWT coefficients which permit low distortion between the watermarked image and the original image. It also shifts a part of the histogram of high frequency subbands and embeds the data by using the created histogram zero point. To prevent the overflows and underflows in the spatial domain, caused by the modification of the DCT coefficients and the DWT coefficients, the histogram modification technique is applied. Therefore, we present a validated method to evaluate and compare the performance of DWT and DCT on task, in terms of data embedding payload and the Peak Signal to Noise Ratio (PSNR) in the medical image. A careful experimental analysis validates the method showing its superiority over the existing methods.

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Approximating sliding windows by cyclic tree-like histograms for efficient range queries

Cited by 9 publications

References 58 publications

Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction

Machine-learning classification techniques for the analysis and prediction of high-frequency stock direction

Identifying streaming frequent items in ad hoc time windows

A novel embedding technique for lossless data hiding in medical images employing histogram shifting method

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