Survey Of LSTM-Based Approaches for Predicting Cryptocurrency Prices

Abstract

Cryptocurrency requests are largely unpredictable, and prognosticating their prices directly is a grueling task. Long Short- Term Memory (LSTM) models have surfaced as a promising tool for prognosticating cryptocurrency prices due to their capability to handle the temporal dependencies in time- series data. This check paper provides an overview of recent exploration on LSTM- grounded approaches for prognosticating cryptocurrency prices. The check paper reviews the current state of the art in LSTM- grounded cryptocurrency price prediction. We bandy the advantages and limitations of LSTM models and punctuate their operations in prognosticating cryptocurrency prices. Also, we give a comprehensive analysis of the different ways and methodologies used in LSTM- grounded cryptocurrency price prediction. The paper examines the datasets and evaluation criteria used in LSTM- grounded cryptocurrency price prediction and identifies the crucial challenges facing this field. The check also discusses the rearmost trends in LSTM- grounded cryptocurrency price prediction exploration and identifies implicit avenues for unborn exploration.