3. OracleX's Core Technology - Realizing Decentralized Prediction

OracleX leverages state-of-the-art technologies to create a highly accurate, reliable, and decentralized prediction platform.

Let's delve into the core technologies that make this possible.

3.1 Data Collection and Preprocessing

The OracleX prediction engine is powered by vast amounts of data. Data sources are broadly categorized into two types: on-chain and off-chain.

On-chain data is collected directly from the blockchain. Through smart contracts, real-time data such as prices, trading volumes, order book information, and liquidity are acquired from major DEXs and DeFi protocols. This data is highly reliable, as it is extremely difficult to tamper with.

Off-chain data, on the other hand, is collected from various external sources. Specifically, this includes cryptocurrency-related news, social media posts, economic indicators, regulatory trends, and even expert reports. This data is collected using technologies such as APIs and web scraping, and is used to understand market sentiment and trends through techniques such as Natural Language Processing (NLP) and sentiment analysis.

The collected data cannot be used directly in prediction models. It needs to undergo a series of cleansing and transformation processes called preprocessing. Specifically, this includes handling missing data, removing outliers, reducing noise, and normalizing the data. Only after this preprocessing is complete, a high-quality dataset that machine learning models can learn from is generated.

3.2 Machine Learning Models

The OracleX prediction engine combines multiple state-of-the-art machine learning models to achieve highly accurate predictions.

In particular, it actively adopts deep learning algorithms such as Long Short-Term Memory (LSTM) networks, which are excellent for handling time-series data, and Transformer models, which are adept at complex pattern recognition.

Since each of these models has different characteristics, using them in combination through an approach called ensemble learning, rather than using them individually, significantly improves prediction accuracy.

Specifically, each model makes predictions independently, and the results are integrated using statistical methods to calculate the final predicted value.

This approach complements the weaknesses of each individual model, resulting in more robust and reliable predictions.

3.3 Leveraging the G.A.M.E Framework

OracleX is developed on the Virtuals Protocol's G.A.M.E (Generative Autonomous Multimodal Entities) framework.

The G.A.M.E framework is a modular agent framework that strongly supports the autonomous behavior of AI agents.

By leveraging this G.A.M.E framework, OracleX has succeeded in automating and streamlining a series of processes such as data collection, preprocessing, model training, prediction execution, and continuous model improvement.

Specifically, by utilizing the High Level Planner (HLP) and Low Level Planner (LLP) functions of G.A.M.E, long-term goal setting and short-term task execution can be performed autonomously.

For example, the HLP can set a long-term goal such as "improve the prediction accuracy of a specific token," and to achieve that goal, the LLP can execute specific tasks such as "collect the latest on-chain data" and "retrain the model."

In this way, the G.A.M.E framework is an important foundational technology that supports the continuous learning and growth of OracleX.