Sentiment Analysis on Twitter to Improve Time Series Contextual Anomaly Detection for Detecting Stock Market Manipulation

2017 ◽  
pp. 327-342 ◽  
Author(s):  
Koosha Golmohammadi ◽  
Osmar R. Zaiane
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Anomaly Detection ◽  
Sentiment Analysis ◽  
Market Manipulation

Time Series for Forecasting Stock Market Prices Based on Sentiment Analysis of Social Media

2021 ◽  
pp. 353-367
Author(s):  
Dakshinamoorthy Karthikeyan ◽  
Babu Aravind Sivamani ◽  
Pavan Kalyan Tummala ◽  
Chamundeswari Arumugam
Keyword(s):  
Social Media ◽  
Time Series ◽  
Stock Market ◽  
Sentiment Analysis ◽  
Market Prices

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Pemodelan Data Time Series Garch(1,1) Untuk Pasar Saham Indonesia

2015 ◽  
Vol 11 (1) ◽  
pp. 13
Author(s):  
Elfa Rafulta ◽  
Roni Tri Putra
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Markets ◽  
Financial Data ◽  
International Stock Markets

This paper introduced a method pengklusteran for financial data. By using the model Heteroskidastity Generalized autoregressive conditional (GARCH), will be estimated distance between the stock market using GARCH-based distance. The purpose of this method is mengkluster international stock markets with different amounts of data.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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