Abstract
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 Naïve 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.
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Devi, S., Devaser, V. (2019). Stock Market Price Prediction Using SAP Predictive Service. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 955. Springer, Singapore. https://doi.org/10.1007/978-981-13-3140-4_13
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DOI: https://doi.org/10.1007/978-981-13-3140-4_13
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