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Found 4 papers, 1 papers with code
Learning ECG signal features without backpropagation
Representation learning has become a crucial area of research in machine learning, as it aims to discover efficient ways of representing raw data with useful features to increase the effectiveness, scope and applicability of downstream tasks such as classification and prediction.
Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation
The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies.
LLT: An R package for Linear Law-based Feature Space Transformation
The presented R package, called LLT, implements this algorithm in a flexible yet user-friendly way.
Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices
The goals of this paper are twofold: (1) to present a new method that is able to find linear laws governing the time evolution of Markov chains and (2) to apply this method for anomaly detection in Bitcoin prices.
