Matching Items (3)
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- All Subjects: Tensor Decomposition
- Creators: Tong, Hanghang
Description
In trading, volume is a measure of how much stock has been exchanged in a given period of time. Since every stock is distinctive and has an alternate measure of shares, volume can be contrasted with historical volume inside a stock to spot changes. It is likewise used to affirm value patterns, breakouts, and spot potential reversals. In my thesis, I hypothesize that the concept of trading volume can be extrapolated to social media (Twitter).
The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock.
In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level.
The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock.
In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level.
ContributorsAwasthi, Piyush (Author) / Davulcu, Hasan (Thesis advisor) / Tong, Hanghang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2015
Description
Tensors are commonly used for representing multi-dimensional data, such as Web graphs, sensor streams, and social networks. As a consequence of the increase in the use of tensors, tensor decomposition operations began to form the basis for many data analysis and knowledge discovery tasks, from clustering, trend detection, anomaly detection to correlationanalysis [31, 38]. It is well known that Singular Value matrix Decomposition (SVD) [9] is used to extract latent semantics for matrix data. When apply SVD to tensors, which have more than two modes, it is tensor decomposition. The two most popular tensor decomposition algorithms are the Tucker [54] and the CP [19] decompositions. Intuitively, they both generalize SVD to tensors. However, one key problem with tensor decomposition is its computational complexity which may cause system bottleneck. Therefore, two phase block-centric CP tensor decomposition (2PCP) was proposed to partition the tensor into small sub-tensors, execute sub-tensor decomposition in parallel and combine the factors from each sub-tensor into final decomposition factors through iterative rerefinement process. Consequently, I proposed Sub-tensor Impact Graph (SIG) to account for inaccuracy propagation among sub-tensors and measure the impact of decomposition of sub-tensors on the other's decomposition, Based on SIG, I proposed several optimization strategies to optimize 2PCP's phase-2 refinement process. Furthermore, I applied SIG and optimization strategies for data focus, data evolution, and focus shifting in tensor analysis. Personalized Tensor Decomposition (PTD) is proposed to account for the users focus given the observations that in many applications, the user may have a focus of interest i.e., part of the data for which the user needs high accuracy and beyond this area focus, accuracy may not be as critical. PTD takes as input one or more areas of focus and performs the decomposition in such a way that, when reconstructed, the accuracy of the tensor is boosted for these areas of focus. A related challenge of data evolution in tensor analytics is incremental tensor decomposition since re-computation of the whole tensor decomposition with each update will cause high computational costs and incur large memory overheads. Especially for applications where data evolves over time and the tensor-based analysis results need to be continuouslymaintained. To avoid re-decomposition, I propose a two-phase block-incremental CP-based tensor decomposition technique, BICP, that efficiently and effectively maintains tensor decomposition results in the presence of dynamically evolving tensor data. I further extend the research focus on user focus shift. User focus may change over time as data is evolving along the time. Although PTD is efficient, re-computation for each user preference update can be the bottleneck for the system. Therefore I propose dynamic evolving user focus tensor decomposition which can smartly reuse the existing decomposition result to improve the efficiency of evolving user focus block decomposition.
ContributorsHuang, shengyu (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Tong, Hanghang (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2021
Description
Many real-world problems, such as model- and data-driven computer simulation analysis, social and collaborative network analysis, brain data analysis, and so on, benefit from jointly modeling and analyzing the underlying patterns associated with complex, multi-relational data. Tensor decomposition is an ideal mathematical tool for this joint modeling, due to its simultaneous analysis of such multi-relational data, which is made possible by the data's multidimensional, array-based nature. A major challenge in tensor decomposition lies with its computational and space complexity, especially for dense datasets. While the process is comparatively faster for sparse tensors, decomposition is still a major bottleneck for many applications. The tensor decomposition process results in dense (hence, large) intermediate results, even when the input tensor is sparse (or small). Noise is another challenge for most data mining techniques, and many tensor decomposition schemes are sensitive to noisy datasets; this is an inevitable problem for real-world data, which can lead to false conclusions. In this dissertation, I develop innovative tensor decomposition algorithms for mining both sparse and dense multi-relational data in a noise-resistant way. I present novel, scalable, parallelizable tensor decomposition algorithms, specifically tuned to be effective for dense, noisy tensors, and which maintain the quality of the resulting analysis. Furthermore, I present results on multi-relational data applications focusing on model- and data-driven computer simulation analysis, as well as social network and web mining, which demonstrate the effectiveness of these tensor decompositions.
ContributorsLi, Xinsheng (Author) / Candan, Kasim S (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria L (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2019