AI Powered Crypto Analytics Agent for Token Insights and Forecasting

The cryptocurrency market is highly volatile and keeps changing rapidly, which makes accurate prediction and decision-making quite difficult. To handle this, we developed an AI-driven crypto analytics agent by combining time-series forecasting models like ARIMA, LSTM, and GRU with NLP-based sentiment analysis to gain real-time market insights.ARIMA worked well for capturing linear trends but struggled with sudden market fluctuations. On the other hand, deep learning models such as LSTM and GRU performed better by learning nonlinear patterns and long-term dependencies. We also used sentiment analysis to understand public mood from social media and news, adding context to numerical predictions.Data was collected from DexScreener, CoinGecko, and social media APIs, and the results were displayed on an interactive dashboard with features like whale transaction tracking, portfolio risk analysis, and sentiment-driven forecasting. Our evaluation showed that combining deep learning with sentiment analysis improved predictive accuracy and provided more practical insights for investors and analysts.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-07
DOI
https://doi.org/10.5281/zenodo.19451422
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI Powered Crypto Analytics Agent for Token Insights and Forecasting

Sangeetha Selvan, Vinit Patil, Raksh Chhatralia, Atharv Velhal et al.
Zenodo (CERN European Organization for Nuclear Research)
Stock Market Forecasting Methods
article

AI Powered Crypto Analytics Agent for Token Insights and Forecasting

Sangeetha Selvan, Vinit Patil, Raksh Chhatralia, Atharv Velhal, Siddhesh Shionde
article en

Abstract

The cryptocurrency market is highly volatile and keeps changing rapidly, which makes accurate prediction and decision-making quite difficult. To handle this, we developed an AI-driven crypto analytics agent by combining time-series forecasting models like ARIMA, LSTM, and GRU with NLP-based sentiment analysis to gain real-time market insights.ARIMA worked well for capturing linear trends but struggled with sudden market fluctuations. On the other hand, deep learning models such as LSTM and GRU performed better by learning nonlinear patterns and long-term dependencies. We also used sentiment analysis to understand public mood from social media and news, adding context to numerical predictions.Data was collected from DexScreener, CoinGecko, and social media APIs, and the results were displayed on an interactive dashboard with features like whale transaction tracking, portfolio risk analysis, and sentiment-driven forecasting. Our evaluation showed that combining deep learning with sentiment analysis improved predictive accuracy and provided more practical insights for investors and analysts.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 6%
Stock Market Forecasting Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.