Regression Models (sezAI)
Last updated
Last updated
Financial Asset Price Forecasting and Modeling Based on Specific Regression Models
The future price predictions of assets in financial markets have always been a deeply debated topic in the literature. In this context, we develop advanced analytical techniques to understand the dynamics of markets and predict future price movements using specific linear regression models for each asset class. These models analyze historical data to provide highly accurate price trend predictions, helping market participants make more informed decisions.
To guide your next investment targets, our AI makes predictions for the following day after the market close, using historical data.
Our AI continuously trains on historical price movements to make accurate predictions. In addition to OHLC values, it also analyzes speculative movements and other price metrics. These patterns are used by our AI, trained on our servers, to generate new predictions. These predictions are compiled into data sets in 6-month packages, allowing our system to adapt to constantly changing financial conditions without being misled by outdated prices that no longer reflect the present.
*Forecast and Prediction According to AI Regression Model.
*The estimates in the table are revised every day after the daily close, with the daily close, sezAI estimates a target value for the altcoins in the table.
*The lower the Errors Ratio value in the column, the more likely the AI's prediction is to be correct.
*In addition, the AI constantly repeats its past predictions on the data in the background, and the Standard Deviation in the column decreases as more correct predictions are made, which lowers the Errors Ratio.
Our data sets are analyzed using two different models: daily and 4-hour closing data. The daily prediction model is used for long-term forecasts (1 week or more). The 4-hour prediction model is used for short-term forecasts (a few days or less).
In addition to BTC and ETH, we also provide the ability to track major coins outside of the data set through charts.
AI prediction models do not provide definitive results. They are designed to help you gain insights into your investments. While the success rate of these predictions is high, they should not be solely relied upon for making investment decisions.
How to Use It
1.Data Input:
Choosing financial asset to forecast and model.
2.Model Selection:
We use AI to choose the most effective model for the selected assets.
3.Analysis and Forecasting:
We run the model to analyze the raw data.
We train the model with AI to achieve more accurate future price predictions.
4.Interpreting Results:
We visualize the results and forecasts through graphs and present them to the platform users.
5.Optimization:
We make improvements to model parameters as needed to obtain more precise predictions.
