/Time-Series-Analysis-on-Bitcoin-Stock-Prices

Time Series Analysis on Bitcoin Price and Bitcoin Price prediction

Primary LanguageJupyter Notebook

Time-Series-Analysis-on-Bitcoin-Stock-Prices

Introduction:

Bitcoin, the world’s most widely recognized cryptocurrency, showcases impressive yet volatile market dynamics. Its ever-changing valuation presents lucrative prospects for investments and speculation, necessitating precise forecasting instruments to navigate the dynamic terrain. This project embarks on a journey to create a data-centric model for predicting Bitcoin’s future prices using Time series analysis and GRU (Gated Recurrent Unit) neural networks.

Scope:

Capitalizing on the abundantly obtainable historic Bitcoin price data ranging from 2015 to 2022, we dive into the depths of Time series analysis to unearth latent patterns, periodic oscillations, and significant associations embedded within the fluctuating price milieu. Armed with this holistic understanding, we proceed to construct a GRU-powered predictive edifice resting comfortably in TensorFlow’s vast machine intelligence landscape.

Key Technologies & Libraries Used

  • Python
  • Jupyter Notebook
  • Tensorflow
  • scikit-learn
  • seaborn
  • matplotlib

Methodology:

  • Acquire and wrangle the Bitcoin price dataset encompassing open, close, high, and low price indices.
  • Visualize the dataset employing state-of-the-art plotting mechanisms to discern evident trends, anomalous occurrences, and recurring motifs.
  • Segregate the dataset chronologically, reserving a portion exclusively for testing our eventual prophetic marvel.
  • Process the raw data points with bespoke normalization procedures, preparing them for the awaiting GRU feast.
  • Design and materialize the GRU topology, configuring hyperparameters attuned to our exclusive case study.
  • Train the fabricated GRU prototype with the processed datapoints, diligently observing convergence and generalizability.
  • Compare the GRU champion against benchmark alternatives, establishing supremacy in terms of predictive finesse.
  • Fine-tune the triumphant GRU juggernaut, subjecting it to grueling trials and tribulations until thoroughly satiated.
  • Release the honed beast onto the test dataset, recording its uncanny knack for peering into Bitcoin’s crystal ball.
  • Package the entire affair into a consumable format, availing the wisdom to eager enthusiasts venturing into the tempestuous realm of crypto-prognostication.

Expected Outcomes:

  • Holistically grasp the characteristics exhibited by Bitcoin’s historical pricing, captivating subtleties, and quirks.
  • Demonstrate the remarkable proficiency inherent in GRUs when juxtaposed against alternative sequential predilections.
  • Illustrate the evolution of Bitcoin’s projected worthiness in tandem with escalating confidence intervals.
  • Empower fellow aficionados to traverse the bewitching labyrinth of Bitcoin fortunes backed by solid GRU-imbued assurance.

Conclusion:

As we venture deeper into the era of decentralized finance, the necessity for erudite forecasting apparatuses intensifies, fueling curiosity and innovation. By marrying the disciplined artistry of Time series analysis with the cerebral prowess of GRUs, we aspire to erect a steadfast bastion heralding the arrival of an era characterized by calculated conjectures and wise deliberations. Join us as we transcend the confines of conventional wisdom, embracing the promising embrace of BitcoinPricePredictionwithGRUs.

Enjoy exploring, and happy coding! 🎉