Futuristic cityscape with Bitcoin-shaped buildings and AI neural network patterns.

Decoding Crypto: Can AI Predict the Next Bitcoin Boom?

Jordan Keane in Tech & Innovation December 2025 • 4 min read.

"Explore how LSTM, SVM, and Polynomial Regression algorithms are being used to forecast cryptocurrency prices and what it means for investors."

In today's fast-paced digital world, the allure of cryptocurrency investments has drawn many away from traditional assets like gold and precious metals. The promise of high returns, however, comes with significant risks. The value of cryptocurrencies fluctuates wildly, presenting a challenge for investors seeking stability.

Fortunately, the rise of advanced technology offers new tools for navigating this volatile landscape. Sophisticated forecasting methods, powered by artificial intelligence (AI), are emerging as potential game-changers. These AI models analyze vast amounts of data to predict future price movements, offering investors a data-driven approach to managing risk.

One such study, spotlighted in the Asian Journal of Engineering, Social and Health, explores the effectiveness of three machine-learning algorithms—Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Polynomial Regression—in predicting cryptocurrency prices. The study compares these models to determine which is most accurate for forecasting Bitcoin's value, using Mean Square Error as the benchmark for comparison.

AI to the Rescue: How Machine Learning is Tackling Crypto Volatility

Futuristic cityscape with Bitcoin-shaped buildings and AI neural network patterns.

The research paper "Prediction of Cryptocurrency Prices Using LSTM, SVM and Polynomial Regression," delves into the use of these algorithms to bring some predictability to the crypto market. Here's a breakdown of each method:

Long Short-Term Memory (LSTM): Inspired by how our brains remember things over time, LSTM is a type of recurrent neural network (RNN) particularly adept at handling time-series data. In simpler terms, LSTM can analyze sequences of data points, such as daily Bitcoin prices, to identify patterns and predict future values. Think of it as teaching a computer to learn from yesterday to predict tomorrow.

How it Works: LSTM networks have memory cells that can store information over long periods, making them excellent at capturing dependencies in sequential data.
Pros: Effective at understanding trends and patterns in time-series data.
Cons: Can be computationally intensive and require significant data for training.

Support Vector Machine (SVM): SVM is a powerful algorithm typically used for classification tasks, but it can also be adapted for regression, or predicting continuous values. SVM works by finding the optimal boundary that separates different classes of data points. In the context of cryptocurrency, SVM can be trained to predict whether the price will go up or down based on various technical indicators.

The Future of Crypto Forecasting: What's Next?

The use of AI and machine learning in cryptocurrency forecasting is still in its early stages, but the potential is enormous. As these technologies evolve and more data becomes available, we can expect even more accurate and sophisticated prediction models to emerge. These advancements promise to empower investors with the insights they need to navigate the crypto market with greater confidence. However, it's important to remember that no prediction model is perfect, and investors should always exercise caution and conduct thorough research before making any investment decisions.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2403.0341,

Title: Prediction Of Cryptocurrency Prices Using Lstm, Svm And Polynomial Regression

Subject: cs.lg q-fin.st

Authors: Novan Fauzi Al Giffary, Feri Sulianta

Published: 05-03-2024

Everything You Need To Know

Why is predicting cryptocurrency prices so challenging?

Predicting cryptocurrency prices is particularly challenging due to the market's inherent volatility. This volatility stems from various factors, including market sentiment, regulatory news, technological advancements, and macroeconomic events. Unlike traditional assets, cryptocurrencies can experience rapid and significant price swings, making it difficult for investors to find stability. Traditional assets like gold provide stability but do not have the high return opportunity potential that many crypto investors seek. The advanced technology of AI models are emerging to help navigate the volatile landscape of the market.

What are the primary AI methods being explored for predicting cryptocurrency prices, and how do they work?

The primary AI methods being explored for predicting cryptocurrency prices are Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Polynomial Regression. LSTM is a type of recurrent neural network (RNN) that excels at handling time-series data by identifying patterns in sequences of data points, such as daily Bitcoin prices. SVM, typically used for classification, can be adapted for regression to predict whether the price will go up or down based on technical indicators. Polynomial Regression, while mentioned, its functionality and limitations are not detailed in the given context. It is used to predict continuous values, similar to SVM.

How does Long Short-Term Memory (LSTM) help in predicting cryptocurrency values, and what are its limitations?

Long Short-Term Memory (LSTM) networks help in predicting cryptocurrency values by using memory cells that store information over long periods. This allows LSTM to analyze sequences of data points, such as daily Bitcoin prices, and identify patterns to predict future values. LSTM is effective at understanding trends in time-series data. However, LSTM can be computationally intensive and requires significant data for training, making it a resource-demanding method. The Support Vector Machine (SVM) and Polynomial Regression algorithms, though less computationally intensive, may not capture the complex temporal dependencies as effectively as LSTM.

In what ways could Support Vector Machine (SVM) be utilized to forecast cryptocurrency price movements?

Support Vector Machine (SVM) can be utilized to forecast cryptocurrency price movements by adapting it for regression tasks. SVM works by finding the optimal boundary that separates different classes of data points. In the context of cryptocurrency, SVM can be trained to predict whether the price will go up or down based on various technical indicators. Although the precise technical indicators and data points are not specified, SVM's capability to discern patterns can provide useful insights. Long Short-Term Memory (LSTM) is more effective at trends, but it has its limitations.

What are the implications of using AI and machine learning for cryptocurrency forecasting, and what should investors keep in mind?

The use of AI and machine learning for cryptocurrency forecasting has significant implications, promising to empower investors with data-driven insights for navigating the crypto market with greater confidence. As these technologies evolve and more data becomes available, even more accurate and sophisticated prediction models are expected to emerge. However, it's crucial to remember that no prediction model is perfect. Investors should always exercise caution and conduct thorough research before making any investment decisions, as relying solely on AI predictions can be risky. The models highlighted, such as Long Short-Term Memory (LSTM) and Support Vector Machine (SVM), offer different strengths, but none guarantee perfect predictions.