Combining Time Series and Sentiment Analysis for Stock Market Forecasting

Proceedings of the 3rd International Conference on Statistics: Theory and Applications ◽

10.11159/icsta21.132 ◽

2021 ◽

Author(s):

Hsiao-Chuan Chou ◽

Kandethody M. Ramachandran

Keyword(s):

Time Series ◽

Stock Market ◽

Sentiment Analysis ◽

Stock Market Forecasting

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

Big Data Analytics and Knowledge Discovery - Lecture Notes in Computer Science ◽

10.1007/978-3-319-64283-3_24 ◽

2017 ◽

pp. 327-342 ◽

Cited By ~ 1

Author(s):

Koosha Golmohammadi ◽

Osmar R. Zaiane

Keyword(s):

Time Series ◽

Stock Market ◽

Anomaly Detection ◽

Sentiment Analysis ◽

Market Manipulation

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Stock market forecasting with financial micro-blog based on sentiment and time series analysis

Journal of Shanghai Jiaotong University (Science) ◽

10.1007/s12204-017-1818-4 ◽

2017 ◽

Vol 22 (2) ◽

pp. 173-179 ◽

Cited By ~ 8

Author(s):

Yinglin Wang

Keyword(s):

Time Series ◽

Stock Market ◽

Time Series Analysis ◽

Series Analysis ◽

Stock Market Forecasting

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AN EFFICIENT HYBRID MACHINE LEARNING METHOD FOR TIME SERIES STOCK MARKET FORECASTING

Neural Network World ◽

10.14311/nnw.2018.28.003 ◽

2018 ◽

Vol 28 (1) ◽

pp. 41-55 ◽

Cited By ~ 5

Author(s):

Omid Mahdi Ebadati E. ◽

Mohammad Mortazavi T.

Keyword(s):

Machine Learning ◽

Time Series ◽

Stock Market ◽

Machine Learning Method ◽

Learning Method ◽

Stock Market Forecasting ◽

Hybrid Machine

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A new procedure in stock market forecasting based on fuzzy random auto-regression time series model

Information Sciences ◽

10.1016/j.ins.2018.02.016 ◽

2018 ◽

Vol 441 ◽

pp. 113-132 ◽

Cited By ~ 27

Author(s):

Riswan Efendi ◽

Nureize Arbaiy ◽

Mustafa Mat Deris

Keyword(s):

Time Series ◽

Stock Market ◽

Time Series Model ◽

Stock Market Forecasting ◽

Auto Regression ◽

Fuzzy Random

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A Neoteric Technique Using ARIMA-LSTM for Time Series Analysis on Stock Market Forecasting

Advances in Intelligent Systems and Computing - Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy ◽

10.1007/978-981-16-5952-2_33 ◽

2021 ◽

pp. 381-392

Author(s):

Hetvi Shah ◽

Vishva Bhatt ◽

Jigarkumar Shah

Keyword(s):

Time Series ◽

Stock Market ◽

Time Series Analysis ◽

Series Analysis ◽

Stock Market Forecasting

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An efficient combination of traditional financial time series and neurofuzzy techniques for stock market forecasting and modeling

IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.2002.1168005 ◽

2005 ◽

Author(s):

F. Abed Rouai ◽

M. Ben Ahmed

Keyword(s):

Time Series ◽

Stock Market ◽

Financial Time Series ◽

Financial Time ◽

Stock Market Forecasting

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A hybrid statistical approach for stock market forecasting based on Artificial Neural Network and ARIMA time series models

2015 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) ◽

10.1109/besc.2015.7365958 ◽

2015 ◽

Cited By ~ 4

Author(s):

R. M. Kapila Tharanga Rathnayaka ◽

D.M.K.N Seneviratna ◽

Wei Jianguo ◽

Hasitha Indika Arumawadu

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Stock Market ◽

Statistical Approach ◽

Time Series Models ◽

Stock Market Forecasting ◽

Artificial Neural

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

10.1007/978-3-030-87007-2_25 ◽

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

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A refined fuzzy time series model for stock market forecasting

Physica A Statistical Mechanics and its Applications ◽

10.1016/j.physa.2008.01.099 ◽

2008 ◽

Vol 387 (12) ◽

pp. 2857-2862 ◽

Cited By ~ 43

Author(s):

Tahseen Ahmed Jilani ◽

Syed Muhammad Aqil Burney

Keyword(s):

Time Series ◽

Stock Market ◽

Time Series Model ◽

Fuzzy Time Series ◽

Stock Market Forecasting

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Stock Prediction using Neural Networks and Time Series Analysis Methods

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit206495 ◽

2020 ◽

pp. 546-551

Author(s):

Mohammad Pardaz Banu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Stock Market ◽

Stock Price ◽

Financial Systems ◽

Stock Market Forecasting ◽

Network Methods ◽

Market Trends ◽

Stock Market Investors

The stock market is considered to be one of the most highly complex financial systems which consist of various components or stocks, the price of which fluctuates greatly with respect to time. Stock market forecasting involves uncovering the market trends with respect to time. All the stock market investors aim to maximize the returns over their investments and minimize the risks associated. There are time series methods such as AR, MA, SARIMAX developed to predict the stock price but neural network methods such as CNN, LSTM also used to predict the stock price. This research paper describes the prediction of stock market using neural network alogorithms and also few time series methods.

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