Analyzing Hybrid Prediction Models in Cryptocurrency Markets

Introduction

The cryptocurrency market is volatile. It requires a strong understanding of different currencies and market data analysis to make profitable investments and trades.

Although traders have been doing a lot of that work manually for years, now there’s automation in place.

Hybrid cryptocurrency prediction models help investors and traders identify patterns among pricing data. Traders can use and understand these data to make investment decisions and close profitable trades.

What Is a Hybrid Cryptocurrency Prediction Model?

Wondering how famous cryptocurrency traders are making money even with the market constantly changing crypto prices? Then, understanding the hybrid prediction model would be required.

Traders and investors are always refining their strategies and investment processes. They have prediction mechanisms and tools to keep them updated on the potential price movements in the near and distant future.

Hybrid cryptocurrency pricing prediction is a sophisticated approach to analyzing cryptocurrency prices. It involves both traditional and modern approaches, making it faster for traders to understand the market.

The hybrid model has different components, such as the ARFIMA (Autoregressive Fractionally Integrated Moving Average), EW (empirical wavelet), and LLWNN (local linear wavelet neural network) approaches, which can be used to create an ARFIMA-EWLLWNN model.

Recent experiments using hybrid cryptocurrency prediction models have shown strong performance. The predictions also outperform the results of classic and traditional models. It offers a good pricing forecast over the long term. Also, it almost minimizes many of the prediction errors other technical models often cause.

But why should investors choose hybrid prediction models over traditional ones? For that, we’ll have to understand how the traditional prediction models work.

Traditional Cryptocurrency Prediction Models

For traditional analysis, experts break down the process into two different aspects –

1. Technical analysis.
2. Fundamental analysis.

Technical Analysis

Technical analysis involves going through the historical pricing movements and how they reflect against financial incidents. Experts used Aroon Indicator, Volume Weighted Average Pricing, and other data points. Trading volumes, relative strength index, and moving average are some data points to consider when running a technical analysis.

Fundamental Analysis

When running the fundamental analysis of a cryptocurrency, experts go through more advanced data related to its mechanism and architecture. This involves understanding the blockchain it’s built on.

The transaction speed, the ability to create dApps and Smart Contracts, and also the consensus mechanisms are critical points of consideration. Even Cryptocurrency regulatory framework is important to consider when thinking of investing.

The people involved in building the blockchain and solving a particular issue through cryptocurrency can also support its value. Other macroeconomic factors and influences can also intensify a cryptocurrency’s market value.

So, when experts are running an analysis on a cryptocurrency, they are looking at all these data. However, with traditional analysis, the process becomes time-intensive since a large part of the evaluation is done manually. So, there’s a chance for a trend to pass before you reach a decision.

AI-Powered Cryptocurrency Prediction Models

The number of investors relying on AI and ML is increasing every day. They are incorporating a hybrid model for prediction using traditional methods with AI models. It helps with crunching a chunk of data and telling users the type of cryptocurrencies they need to invest in.

Technologies like neural networks, Support Vector Machines (SVM), and decision trees are helping users craft the road toward pricing prediction. This is very useful for daily trading and long-term investments.

AI has made bulk analysis of pricing trend-related data easier, which gives cryptocurrency traders significant support in conducting regular trading.

Leveraging Both Traditional & Hybrid Prediction Models in Crypto Trend

A hybrid model of cryptocurrency prediction helps combine different new technologies with human intervention. Here’s how experts are making it work with different components –

Machine learning helps the AI learn about pricing data and changes in the pricing history of specific cryptocurrencies. Here’s how it helps the investors –

LSTM is a type of recurrent neural network. It makes time series forecasting hassle-free, using its functionalities to memorize long-term dependencies.

Next, ANN or artificial neural networks help build a complex correlation between both inputs and output. ANN works like humans since it mimics the biological neural network present in humans.

CNN or convolutional neural networks help process images. However, when using it for a hybrid cryptocurrency prediction model, traders can utilize CNN for time series analysis. Here, they treat the time series data as images to run this analysis.

How do You Use the Hybrid Cryptocurrency Prediction Model for Trading?

Hybrid prediction models utilize a combination of different machine-learning techniques. It utilizes massive chunks of data and analyzes relations between them to provide trading decisions.

That’s how the hybrid prediction models can help in cryptocurrency investment. Here is how you can utilize hybrid prediction models for cryptocurrency trading predictions.

More Accurate Predictions

Compared to different structures and cryptocurrency prediction models, LSTM and neural networks with metaheuristic algorithms have shown better prediction results. The predicted results are more accurate.

Several studies suggest that the hybrid prediction models returned 85% accurate results compared to the 38% return with manual predictions.

Volatility Forecasting

Hybrid GARCH models can help with accurate Bitcoin price volatility forecasting. They combine double—and triple-GARCH-type models with deep learning models. In addition, they integrate features like RSI and RVI and improve those forecasts.

Identifying Trends

With machine learning models, hybrid prediction models can showcase complex data relationships. It might be difficult to identify such relationships with human observation, where AI steps in and helps identify the market trends.

Reducing Trading Risks

With AI, it’s easier to provide more accurate predictions and volatility assessments. It can lead to better trading decisions and more information, reducing risks in the process.

Outlier Detection

With hybrid models, it’s easier to minimize large deviations and improve the reliability of predictions.

Adaptability

With automated prediction tools, it’s easier to adapt to the features of the cryptocurrency market. It helps support investor strategies during rapid fluctuations in cryptocurrency pricing. Get more insights on hybrid predictions from Coinfomania to improve your cryptocurrency analysis skills.

Aside from all these functionalities, hybrid crypto prediction models also help cryptocurrency traders get real-time pricing predictions. As a result, they can close more trades, bringing in more daily profits.

New World for Traders

Even with hybrid prediction, cryptocurrency investments and trades can be risky. However, the use of AI and hybrid prediction makes the prediction accuracy better. As a result, traders and investors need not waste their time on manual analysis.

Bringing these technologies to cryptocurrency trading can make crypto trading more of a mainstream method of making money. In addition, as technologies improve, we can see a more advanced version of the hybrid prediction model.