In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.

In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. In this study, we investigate a framework with a set of advanced machine learning forecasting methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that the gated recurring unit (GRU) model with recurrent dropout performs better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.

An optimal approach to anonymization using small data is proposed in this study. Map Reduce is a big data processing framework used across distributed applications. Prior to the development of a map reduce framework, data are distributed and clustered using a hybrid clustering algorithm. The algorithm used for grouping together similar techniques utilises the k-means clustering algorithm, along with the MFCM clustering algorithm. Clustered data is then fed into the map reduce frame work after it has been clustered. In order to guarantee privacy, the optimal k anonymization method is recommended. When using generalisation and randomization, there are two techniques that can be employed: K-anonymity, which is unique to each, depends on the type of the quasi identifier attribute. Our method replaces the standard k anonymization process by employing an optimization algorithm that dynamically determines the optimal k value. This algorithm uses the Modified Grey Wolf Optimization (MGWO) algorithm for optimization. The memory, execution time, accuracy, and error value are used to assess the recommended method’s practise. This experiment has shown that the suggested method will always finish ahead of the existing method by using the least amount of time while ensuring the greatest level of security. The current technique gets the lowest accuracy and the privacy proposed achieves the maximum accuracy while compared to the current technique. The solution is implemented in Java with Hadoop Map-Reduce, and it is tested and deployed in the cloud on Google Cloud Platform.

No abstract