Combining Techniques: A Look at Hybrid Cryptocurrency Prediction Models

Table of Contents

Introduction

Cryptocurrency investments can be difficult. Especially when you’re trying to predict the price of Doge coin and spending hours on graphs, charts and historical data.

You’ll gain some knowledge by reading a crypto’s whitepaper and learning all about its intrinsic value. But that’s not enough to start your investment.

Especially if you want to invest now, you need suggestions (of course, data-driven) at the ready. That’s where hybrid cryptocurrency prediction models come into play.

These are prediction models that help investors make the right choice when building their cryptocurrency portfolio.

Understanding Hybrid Cryptocurrency Prediction Models

The cryptocurrency market is volatile. But investors and traders still manage to make profit out of the volatile world of crypto. They use several strategies, and prediction mechanisms to foresee the changes in price.

However, with the world of technology being fast paced (faster everyday than before) it’s quite difficult to start a journey as a cryptocurrency investor. Price prediction of any token would require more sophisticated approach which is why there’s a hybrid cryptocurrency prediction model.

It’s a combination of technologies and strategies and artificial intelligence to predict pricing with pinpoint accuracy.

We have explored the concept of hybrid prediction models, different components, and their applications, and challenges. Keep reading to learn more about this new trend in the world of cryptocurrency.

Traditional Cryptocurrency Prediction Models

Historically, there have been two different aspects of cryptocurrency prediction: 1. Technical analysis, 2. Fundamental Analysis.

Technical analysis:

The technical analysis of a cryptocurrency involves analyzing all the number and graph related data regarding a coin. Investors look for data points like trading volumes, relative strength index, or moving average. They use this data to forecast future price movements based on the previous data.

Fundamental Analysis:

Fundamental Analysis of a cryptocurrency is a lot different than technical analysis. It depends on the intrinsic value of cryptocurrency. Investors look into the potential of the Blockchain security and the problems those decentralized applications made on the blockchain can solve. The team expertise, innovative technologies, and the macroeconomic influences play a big role in this.

Both technical and fundamental analyses of cryptocurrency are useful and add to certain levels of price prediction. Yes, they work. However, with the current pace of technological improvement, these two methods are falling short. Hence, the hybrid cryptocurrency prediction model is in place.

We have discussed how it came into play in the section below –

Machine Learning & AI

Before AI investors and market experts used to manually go through the data charts, historical changes in cryptocurrency price and the graphs. Simply put their analysis of cryptocurrency price movement was purely manual.

But, after AI and ML came into the scene, lot of the heavy work related to price prediction became automated. AI has revolutionized financial forecasting by bulk analyzing data. It’s helping see patterns within cryptocurrency price changes.

Algorithms like neural networks, decision trees, and SVM (Support Vector Machines) have changed the way predictions are made.

With these technologies, experts can analyze vast amounts of data within a day. Thanks to this massive development on the AI front, cryptocurrency prices have changed and their skills to learn from the data can compound overtime.

As a result, it will help in predicting cryptocurrency prices and empower more people to adopt cryptocurrency.

Hybrid Models in Cryptocurrency Prediction: An Overview

A hybrid model of cryptocurrency prediction is a combination of different new technologies with human intervention.

Machine Learning Algorithm

There’s a machine learning algorithm at the heart of hybrid cryptocurrency pricing models. It’s there to complete specific tasks related to the prediction. Here’s what it does for investors:

Long Short-Term Memory (LSTM)

It’s a type of recurrent neural network that makes time series forecasting easier thanks to its capability to memorize long-term dependencies.

Artificial Neural Networks (ANN)

It helps model complex relationships between inputs and outputs. This is done by mimicking the neural networks that are biologically present in humans.

Convolutional Neural Networks (CNN)

CNN is primarily used for processing images. But, in hybrid model, investors can apply it for time series analysis. Here, they run this analysis by treating time series data as images.

How can Hybrid Prediction Models Help in Crypto Investment?

Now that you know what this technology is, it’s a fair question to ask – how will hybrid prediction models help build crypto investment portfolios?

We have explained that in the two points below –

Forecasting Emerging Trends

The biggest crypto investment trends start with very minor changes. It’s almost impossible to stay ahead of those changes with manual observation. With artificial intelligence and machine learning techniques, it’s possible to stay ahead of the trend.

In fact, the hybrid prediction model can help grab an opportunity as soon as it arrives in the crypto market. Data analysis using artificial intelligence can uncover insights and help track trends.

More Focus on Hybrid Models

As the hybrid models start to become more accurate with new experiments, investors may start investing in those models themselves.

Many of these models will integrate new algorithms and other technologies to help with better pricing forecasts. The predictive accuracy of these models can also stay at the forefront of the investment world.

Successful Implementations of Hybrid Prediction Model

In recent years, there have been several instances of successful use of hybrid models for investment. Here are some examples that might help –

LSTM + Technical Indicators

The recent experiment with hybrid prediction models has shown significant improvements. Experts have combined the LSTM networks with RSI and MACD. The integration of these technical integrations improves prediction accuracy by a significant margin. The results are way better compared to the traditional method of prediction.

Ensemble Learning

As per a study, an ensemble model that combines ARIMA and machine learning can outperform individual models in predicting crypto prices. In fact, experts can do this over different time horizons.

Final Words

Hybrid cryptocurrency models showcase a new age of forecasting methods. This method can make the volatile world of cryptocurrency a little more stable. By combining machine learning and analytical techniques, these models help enhance predictive accuracy while addressing specific limitations within single-method approaches.

With time technology will be involved and this methodology will also integrate new tools and architectures. We can only expect it to evolve. Until then we can rely on hybrid prediction models and leverage its prediction for cryptocurrency investments.