scholarly journals Time Series Analysis of Online Public Opinions in Colleges and Universities and its Sustainability

2019 ◽  
Vol 11 (13) ◽  
pp. 3546 ◽  
Author(s):  
Wei He ◽  
Yuan Fang ◽  
Reza Malekian ◽  
Zhixiong Li
Keyword(s):  
Time Series ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Colleges And Universities ◽  
Lessons Learned ◽  
Short Term ◽  
Trend Prediction ◽  
Internet Applications ◽  
Public Opinions ◽  
Short Term Trend

With the quick penetration of Internet applications, online media have become an important carrier of public opinions. The opinions and comments expressed by young college students—one of the most active netizen groups—on the Internet have turned out to be an essential part of the online public opinions in colleges and universities. However, the existing systems generally employ simple statistical methods to analyze the effect of online public opinions on the image and reputation development of colleges and universities without taking account of other factors, such as the hotness characteristics of online public opinions and semantic information. Therefore, on the basis of Public Opinion Hotness Index and time series-based trend analysis, as well as the topics extracted using the latent Dirichlet allocation (LDA) topic model, this study aims to improve the analysis performance on the online public opinions in colleges and universities using short-term trend prediction results. The experience and lessons learned from a real case may provide strong data support and feasible suggestions for colleges and universities in analyzing and guiding the online public opinions.

Short-term trend prediction in financial time series data

2018 ◽  
Vol 61 (1) ◽  
pp. 397-429
Author(s):  
Mustafa Onur Özorhan ◽  
İsmail Hakkı Toroslu ◽  
Onur Tolga Şehitoğlu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Series Data ◽  
Short Term ◽  
Trend Prediction ◽  
Term Trend ◽  
Financial Time ◽  
Short Term Trend

scholarly journals Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series

2020 ◽  
Vol 34 (02) ◽  
pp. 1395-1402
Author(s):  
Dongkuan Xu ◽  
Wei Cheng ◽  
Bo Zong ◽  
Dongjin Song ◽  
Jingchao Ni ◽  
...  
Keyword(s):  
Time Series ◽  
Shared Memory ◽  
Multivariate Time Series ◽  
Temporal Patterns ◽  
Series Data ◽  
Short Term ◽  
Trend Prediction ◽  
Multiple Variables ◽  
Parameter Sharing

The problem of learning and forecasting underlying trends in time series data arises in a variety of applications, such as traffic management, energy optimization, etc. In literature, a trend in time series is characterized by the slope and duration, and its prediction is then to forecast the two values of the subsequent trend given historical data of the time series. For this problem, existing approaches mainly deal with the case in univariate time series. However, in many real-world applications, there are multiple variables at play, and handling all of them at the same time is crucial for an accurate prediction. A natural way is to employ multi-task learning (MTL) techniques in which the trend learning of each time series is treated as a task. The key point of MTL is to learn task relatedness to achieve better parameter sharing, which however is challenging in trend prediction task. First, effectively modeling the complex temporal patterns in different tasks is hard as the temporal and spatial dimensions are entangled. Second, the relatedness among tasks may change over time. In this paper, we propose a neural network, DeepTrends, for multivariate time series trend prediction. The core module of DeepTrends is a tensorized LSTM with adaptive shared memory (TLASM). TLASM employs the tensorized LSTM to model the temporal patterns of long-term trend sequences in an MTL setting. With an adaptive shared memory, TLASM is able to learn the relatedness among tasks adaptively, based upon which it can dynamically vary degrees of parameter sharing among tasks. To further consider short-term patterns, DeepTrends utilizes a multi-task 1dCNN to learn the local time series features, and employs a task-specific sub-network to learn a mixture of long-term and short-term patterns for trend prediction. Extensive experiments on real datasets demonstrate the effectiveness of the proposed model.

scholarly journals Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

2019 ◽  
Vol 9 (20) ◽  
pp. 4460 ◽  
Author(s):  
Francesco Rundo
Keyword(s):  
Reinforcement Learning ◽  
High Frequency ◽  
Foreign Exchange Market ◽  
Learning Algorithm ◽  
High Frequency Trading ◽  
Short Term ◽  
Trend Prediction ◽  
Trading Systems ◽  
Term Trend ◽  
Short Term Trend

High-frequency trading is a method of intervention on the financial markets that uses sophisticated software tools, and sometimes also hardware, with which to implement high-frequency negotiations, guided by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on. HFT strategies have reached considerable volumes of commercial traffic, so much so that it is estimated that they are responsible for most of the transaction traffic of some stock exchanges, with percentages that, in some cases, exceed 70% of the total. One of the main issues of the HFT systems is the prediction of the medium-short term trend. For this reason, many algorithms have been proposed in literature. The author proposes in this work the use of an algorithm based both on supervised Deep Learning and on a Reinforcement Learning algorithm for forecasting the short-term trend in the currency FOREX (FOReign EXchange) market to maximize the return on investment in an HFT algorithm. With an average accuracy of about 85%, the proposed algorithm is able to predict the medium-short term trend of a currency cross based on the historical trend of this and by means of correlation data with other currency crosses using techniques known in the financial field with the term arbitrage. The final part of the proposed pipeline includes a grid trading engine which, based on the aforementioned trend predictions, will perform high frequency operations in order to maximize profit and minimize drawdown. The trading system has been validated over several financial years and on the EUR/USD cross confirming the high performance in terms of Return of Investment (98.23%) in addition to a reduced drawdown (15.97 %) which confirms its financial sustainability.

A short-term trend prediction model of topic over Sina Weibo dataset

2013 ◽  
Vol 28 (3) ◽  
pp. 613-625 ◽  
Author(s):  
Juanjuan Zhao ◽  
Weili Wu ◽  
Xiaolong Zhang ◽  
Yan Qiang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Short Term ◽  
Trend Prediction ◽  
Sina Weibo ◽  
Term Trend ◽  
Short Term Trend

Coauthorship network-based literature recommendation with topic model

2017 ◽  
Vol 41 (3) ◽  
pp. 318-336 ◽  
Author(s):  
San-Yih Hwang ◽  
Chih-Ping Wei ◽  
Chien-Hsiang Lee ◽  
Yu-Siang Chen
Keyword(s):  
Hybrid Method ◽  
Information Needs ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Database Systems ◽  
Short Term ◽  
Content Type ◽  
Coauthorship Network ◽  
Coauthorship Networks ◽  
Hybrid Recommendation

Purpose The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles. Previous works on recommending articles to satisfy users’ short-term interests have utilized article content, usage logs, and more recently, coauthorship networks. The usefulness of coauthorship has been demonstrated by some research works, which, however, tend to adopt a simple coauthorship network that records only the strength of coauthorships. The purpose of this paper is to enhance the effectiveness of coauthorship-based recommendation by incorporating scholars’ collaboration topics into the coauthorship network. Design/methodology/approach The authors propose a latent Dirichlet allocation (LDA)-coauthorship-network-based method that integrates topic information into the links of the coauthorship networks using LDA, and a task-focused technique is developed for recommending literature articles. Findings The experimental results using information systems journal articles show that the proposed method is more effective than the previous coauthorship network-based method over all scenarios examined. The authors further develop a hybrid method that combines the results of content-based and LDA-coauthorship-network-based recommendations. The resulting hybrid method achieves greater or comparable recommendation effectiveness under all scenarios when compared to the content-based method. Originality/value This paper makes two contributions. The authors first show that topic model is indeed useful and can be incorporated into the construction of coaurthoship-network to improve literature recommendation. The authors subsequently demonstrate that coauthorship-network-based and content-based recommendations are complementary in their hit article rank distributions, and then devise a hybrid recommendation method to further improve the effectiveness of literature recommendation.

Winning the Argument and Moving the Fight: The Legacy of a Grassroots Humanitarian

Public Voices ◽  
2016 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Mary Coleman
Keyword(s):  
Higher Education ◽  
United States ◽  
Winning Strategy ◽  
Supreme Court Decision ◽  
The United States ◽  
Colleges And Universities ◽  
Lessons Learned ◽  
The Political ◽  
Political Actors ◽  
Trade Offs

The author of this article argues that the two-decades-long litigation struggle was necessary to push the political actors in Mississippi into a more virtuous than vicious legal/political negotiation. The second and related argument, however, is that neither the 1992 United States Supreme Court decision in Fordice nor the negotiation provided an adequate riposte to plaintiffs’ claims. The author shows that their chief counsel for the first phase of the litigation wanted equality of opportunity for historically black colleges and universities (HBCUs), as did the plaintiffs. In the course of explicating the role of a legal grass-roots humanitarian, Coleman suggests lessons learned and trade-offs from that case/negotiation, describing the tradeoffs as part of the political vestiges of legal racism in black public higher education and the need to move HBCUs to a higher level of opportunity at a critical juncture in the life of tuition-dependent colleges and universities in the United States. Throughout the essay the following questions pose themselves: In thinking about the Road to Fordice and to political settlement, would the Justice Department lawyers and the plaintiffs’ lawyers connect at the point of their shared strength? Would the timing of the settlement benefit the plaintiffs and/or the State? Could plaintiffs’ lawyers hold together for the length of the case and move each piece of the case forward in a winning strategy? Who were plaintiffs’ opponents and what was their strategy? With these questions in mind, the author offers an analysis of how the campaign— political/legal arguments and political/legal remedies to remove the vestiges of de jure segregation in higher education—unfolded in Mississippi, with special emphasis on the initiating lawyer in Ayers v. Waller and Fordice, Isaiah Madison

scholarly journals Short-term household load forecasting based on Long- and Short-term Time-series network

2021 ◽  
Vol 7 ◽  
pp. 58-64
Author(s):  
Xifeng Guo ◽  
Ye Gao ◽  
Yupeng Li ◽  
Di Zheng ◽  
Dan Shan
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Short Term
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