Stock Market Price Prediction Using SAP Predictive Service

被引:0
|
作者
Devi, Sanjana [1 ]
Devaser, Virrat [1 ]
机构
[1] Lovely Profess Univ, Dept Comp Sci & Engn, Phagwara, Punjab, India
来源
ADVANCED INFORMATICS FOR COMPUTING RESEARCH, ICAICR 2018, PT I | 2019年 / 955卷
关键词
Data forecasting; Stock market price prediction; Cloud appliance library; Amazon web services; SAP predictive services;
D O I
10.1007/978-981-13-3140-4_13
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Stock market price prediction is one of the most active areas of research among analysts from last few decades. Stock market price prediction is nothing but a process of trying to find the stock cost price for next trading day or next few days. A good prediction strategy may help to yield more profit. Different techniques and instruments are utilized to forecast the stock market price like artificial neural system, fuzzy logic, machine learning, Support Vector Machine, ARIMA model, R programming. Different algorithms are utilized to execute all these methods more precisely like Naive Bayes, K-means, genetic Algorithms and Data mining algorithms and so on. The main intention is to increase the accuracy of forecast the stock market. Here, we are going to create a model with the help of Amazon web services, SAP Cloud Appliance Library. The Model is integrated with cloud to manage the dataset easily. SAP predictive services help to predict the future outcome in better way. Beauty of this model is it can handle large amount of data very easily. Like if user have historical data in the form of big data, it can not only easily managed in cloud environment but analyzing those data in very short time span is also possible with the help of SAP, because SAP is in-memory database.
引用
收藏
页码:135 / 148
页数:14
相关论文
共 50 条
  • [31] Combining market-guided patterns and mamba for stock price prediction
    Chang, Yanshuo
    Lu, Wei
    Xue, Feng
    Lu, Xinyu
    ALEXANDRIA ENGINEERING JOURNAL, 2025, 113 : 287 - 293
  • [32] Testing price prediction models in dynamically configurable artificial stock market
    Malik, S
    Ahmad, U
    Ali, A
    Abbasi, F
    Rauf, F
    IC-AI '04 & MLMTA'04 , VOL 1 AND 2, PROCEEDINGS, 2004, : 671 - 677
  • [33] Combinatorial Impact of Technical Indicators on Price Prediction in Colombo Stock Market
    Caldera, H. A.
    Lavanya, W. P. A.
    2020 20TH INTERNATIONAL CONFERENCE ON ADVANCES IN ICT FOR EMERGING REGIONS (ICTER-2020), 2020, : 256 - 261
  • [34] The predictive power of "Head-and-Shoulders" price patterns in the US stock market
    Savin, Gene
    Weller, Paul
    Zvingelis, Janis
    JOURNAL OF FINANCIAL ECONOMETRICS, 2007, 5 (02) : 243 - 265
  • [35] Combined deep learning classifiers for stock market prediction: integrating stock price and news sentiments
    Shilpa, B. L.
    Shambhavi, B. R.
    KYBERNETES, 2023, 52 (03) : 748 - 773
  • [36] State transition characteristics and prediction of Stock Price: An Empirical Research on Shanghai A Share Stock Market
    Li Yunhong
    Wei Yu
    PROCEEDINGS OF THE 6TH (2014) INTERNATIONAL CONFERENCE ON FINANCIAL RISK AND CORPORATE FINANCE MANAGEMENT, VOLS. I AND II, 2014, : 485 - 489
  • [37] Predicting Stock Price Returns using Microblog Sentiment for Chinese Stock Market
    Sun, Tong
    Wang, Jia
    Zhang, Pengfei
    Cao, Yu
    Liu, Benyuan
    Wang, Degang
    2017 3RD INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING AND COMMUNICATIONS (BIGCOM), 2017, : 87 - 96
  • [38] Prediction of Stock Market Indices - Using SAS
    Reddy, B. Siddhartha
    2010 2ND IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND FINANCIAL ENGINEERING (ICIFE), 2010, : 112 - 116
  • [39] Stock Market Prediction Using Hybrid Approach
    Rajput, Vivek
    SarikaBobde
    2016 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA), 2016, : 82 - 86
  • [40] Prediction of Stock Market Using Artificial Intelligence
    Shah, Hemil N.
    2019 IEEE 5TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2019,