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Cryptocurrency Price Prediction Using Long Short Term Memory Modeling and Social Media Sentiment

This paper presents an optimized solution for predicting cryptocurrency prices and market directions by utilizing long short-term memory (LSTM) modeling and social media sentiment analysis. We aim to minimize prediction errors and achieve scalable performance for multiple cryptocurrencies.

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Cryptocurrency Price Prediction Using Long Short Term Memory Modeling and Social Media Sentiment

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  1. Cryptocurrency Price Prediction Using Long Short Term Memory Modeling and Social Media Sentiment O.P Akomolafe, Oluwafikayo Sanni Presenting: Oluwafikayo Sanni 3rd Biennial Conference on TOKI (Transition From Observation To Knowledge To Intelligence) 15th to 16th August 2019 New Engineering Lecture Theatre, University of Lagos

  2. Introduction

  3. Introduction

  4. Literature Review

  5. Research Motivation Predicting cryptocurrency prices and market directions is not a new problem, however existing solutions in literature generally have Wide margins of errors in prediction Conservative loss functions and auto-regressive models, A general inability to scale to predict prices and market directions for multiple tokens while keeping performance relatively stable. This work aims to provide an optimized solution that provides low margins of errors in predictions and scalable performance for multiple crypto-currencies

  6. Our ApproachSocial Media Sentiment Extraction and Analysis Generate Average Daily Sentiment Search Keyword with Date De-limiter Time Series Daily Sentiment Data Twitter API Sentiment Polarity Detection with TextBlob Regular Expression Filtering and Data Cleaning Daily Time Series Tweets

  7. Our ApproachModel Flow

  8. Our Approach

  9. Our Approach

  10. Our Approach

  11. Result Summary

  12. ResultComparison The Table Below Shows Optimal Results in Previous Works

  13. ResultComparison, Bitcoin Accuracy

  14. ResultComparison, Ethereum Accuracy

  15. ResultComparison, Bitcoin Prediction; Margin of Error

  16. ResultComparison, Ethereum Prediction; Margin of Error

  17. Conclusion In predicting absolute number of price reductions and increments, our model performed very poorly when compared the best of the existing models. Our Model however possess a comparative advantage of having a higher accuracy when it comes to predicting the margins of these fluctuations. Our model also has a relatively uniform performance for all three tokens. Meaning its behaviour could be scalable for predicting other tokens with proper training

  18. References

  19. References

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