A.I Price Prediction Oracle

A.I Price prediction Oracle

In predicting the prices of commodities such as Stone, Wood and Gold, Solland A.I Market Price prediction uses machine learning and artificial intelligence (A.I) models to analyze data and make future price predictions of the source The resources that Users are exploiting help NFT holders realize the growth market in Solland. Specifically, we use the support vector machine (SVM) model to build a model to predict the prices of daily items.

An SVM model to predict the prices of Stone, Wood, and Gold, we need to map the input data into multidimensional space based on the features of the item. Then, we train the SVM model to find the optimal separating hyperplane between the different types of items. When new data is available, we use the trained model to predict the price of the item based on its features.

The general formula of the SVM model to predict the price of an item can be represented as:

$Y = \sum_{i=1}^{n} \alpha_i y_i K(X_i, X) + b$

Where:

$Y$ is the price of the item to be predicted.
$n$ is the number of training data points.
$\alpha_i$ the Lagrange multiplier.
$y_i$ is the label of the i-th data point (-1 or 1).
$X_i$ is the i-th training data point.
$X$ is the data point to be predicted.
$K(X_i, X)$ is the kernel function, often a nonlinear kernel like polynomial kernel, Gaussian kernel (RBF), or sigmoid kernel.
$b$ is the bias term.

Market Price

Each Resource (wood, stone, gold) has a fluctuating price based on the amount of circulation of the resource and the demand for owning the types of NFTs corresponding to that resource.
The total number of Wood, Stone, and Gold Mines in the game and the amount of Wood, Stone, and Gold resources in circulation will be displayed.
The player selling resources agrees with the price when activating the order and entering the queue (minimum 24 hours for the 1st sale).
Market oracle is automatically adjusted based on the supply of each type of resource and the amount of reward in the reward pool (aka. Game revenue). For example, if wood is inflating, the price will decrease; On the contrary, if few people mine gold, the price will be high.
Market Oracle is calculated to ensure that the amount of rewards does not cause excessive inflation, leading to the reward pool being exhausted but still bringing players large profits.

Target

Balance supply and demand, ensure reasonable benefits, avoid inflation.
Bring profits to players and maintain sustainable game development.

Highlights

Transparent and automated trading platform.
Prices reflect the current market.
Balance the interests of players and developers.

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Last updated 1 year ago

A.I Price prediction Oracle

The general formula of the SVM model to predict the price of an item can be represented as:

Y = \sum_{i=1}^{n} \alpha_i y_i K(X_i, X) + b

Where:

$Y$ is the price of the item to be predicted.

$n$ is the number of training data points.

$\alpha_i$ the Lagrange multiplier.

$y_i$ is the label of the i-th data point (-1 or 1).

$X_i$ is the i-th training data point.

$X$ is the data point to be predicted.

$K(X_i, X)$ is the kernel function, often a nonlinear kernel like polynomial kernel, Gaussian kernel (RBF), or sigmoid kernel.

$b$ is the bias term.

Market Price

Each Resource (wood, stone, gold) has a fluctuating price based on the amount of circulation of the resource and the demand for owning the types of NFTs corresponding to that resource.

The total number of Wood, Stone, and Gold Mines in the game and the amount of Wood, Stone, and Gold resources in circulation will be displayed.

The player selling resources agrees with the price when activating the order and entering the queue (minimum 24 hours for the 1st sale).

Market oracle is automatically adjusted based on the supply of each type of resource and the amount of reward in the reward pool (aka. Game revenue). For example, if wood is inflating, the price will decrease; On the contrary, if few people mine gold, the price will be high.

Market Oracle is calculated to ensure that the amount of rewards does not cause excessive inflation, leading to the reward pool being exhausted but still bringing players large profits.

Target

Balance supply and demand, ensure reasonable benefits, avoid inflation.

Bring profits to players and maintain sustainable game development.

Highlights

Transparent and automated trading platform.

Prices reflect the current market.

Balance the interests of players and developers.