Stock Market Real Time Recommender Model Using Apache Spark Framework

Seif, Mostafa Mohamed; Hamed, Essam M. Ramzy; Hegazy, Abdelfatah

doi:10.1007/978-3-319-74690-6_66

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…As discussed in the above-detailed study, input parameters provided to any machine learning algorithm play a very crucial role to forecast the stock values. Based on the findings claimed in [15], the prediction accuracy for the stock market achieved by top authors I is 70.21% using Logistic regression. So there seems a scope for improvement.…”

Section: B Proposed Methodologymentioning

confidence: 93%

“…The authors in [15] proposed two prediction models supported news, tweets, and historical price. One model for online mode and another for offline mode.…”

Section: B Big Data Framework and Machine Learning Techniquesmentioning

confidence: 99%

“…The data generated by these sources are very huge and needs to get analyzed to see how it can affect the stock market. As this data is unstructured so along with analysis, this data needs special handling for its storage and processing [15]. For the purpose, Researchers move towards big data approach for the prediction task.…”

Section: Introductionmentioning

confidence: 99%

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Short Term Stock Movements with Big Data and Market Sentiments Analytics

Kalra¹,

Gupta²,

Prasad³

2019

IJITEE

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Machine Learning Techniques and Big Data analytics are two central points of data science. Big Data is important for organizations to gain insights into it and machine learning techniques are one of the substantial assets for analyzing a massive amount of data. In this paper, a framework has been proposed to improve the short term stock trend prediction accuracy using Logistic Regression model by means of qualitative and quantitative data. This paper makes a comprehensive survey of stock market trend prediction with the accumulation of various data sources by applying machine learning techniques and by using big data analytics approach. The model has been implemented in Big data Framework with Hadoop and Apache Spark. For qualitative data Tweets sentiments and news sentiments has been taken in to account and for quantitative data Google trends and historical data are considered. The proposed system has enhanced the prediction accuracy about 3-4 % in comparison to existing models by supplying Google trend as input data in addition to market sentiments and historical data. The implemented model can help the investors to take short term decisions to make money in the security market and the survey would help in finding the most effective resources which overly influence the stock prices.

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Section: B Proposed Methodologymentioning

confidence: 93%

“…The authors in [15] proposed two prediction models supported news, tweets, and historical price. One model for online mode and another for offline mode.…”

Section: B Big Data Framework and Machine Learning Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Short Term Stock Movements with Big Data and Market Sentiments Analytics

Kalra¹,

Gupta²,

Prasad³

2019

IJITEE

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“…Therefore, we treat our objective as a big data problem and employ big data tools to ensure scalability and performance. We take inspiration from [18] which uses Apache Spark and Hadoop HDFS to forecast stock market trends based on social media sentiment and historical price. Similarly, we leverage the performance of Apache Spark RDDs and the persistence of Apache Hive to build a solution that is fast, accurate and fault-tolerant.…”

Section: Related Workmentioning

confidence: 99%

KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments

Mohapatra

Ahmed

Alencar

2019

2019 IEEE International Conference on Big Data (Big Data)

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Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification. However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handle massive heterogeneous data volumes, including social media sentiments, while supporting fault tolerance and persistence in real time, and provide real-time adaptation of learning algorithms to cope with new price and sentiment data. In this paper we introduce KryptoOracle, a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform is based on (i) a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way; (ii) an approach that supports sentiment analysis which can respond to large amounts of natural language processing queries in real time; and (iii) a predictive method grounded on online learning in which a model adapts its weights to cope with new prices and sentiments. Besides providing an architectural design, the paper also describes the KryptoOracle platform implementation and experimental evaluation. Overall, the proposed platform can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and everlarger financial data volume and variety.

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The recommendation service of the shareholding for fund companies based on improved collaborative filtering method

Cui¹,

Feng

et al. 2019

Procedia Computer Science

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Stock Market Real Time Recommender Model Using Apache Spark Framework

Cited by 6 publications

References 6 publications

Short Term Stock Movements with Big Data and Market Sentiments Analytics

Short Term Stock Movements with Big Data and Market Sentiments Analytics

KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments

The recommendation service of the shareholding for fund companies based on improved collaborative filtering method

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