Stock Market Prediction is about developing models to predict the further development of a financial asset such as, a stock. There are different approaches to this. In classical machine learning, a prediction model is trained on the basis of historical data in the hope that this will provide information about future developments.
Affinity propagation is an unsupervised clustering technique that stands out from other clustering approaches by its capacity to determine the number of clusters in a dataset. This tutorial demonstrates this capacity by applying the method to analyze the crypto market … Read more
In time series regression, the number of neurons in the final output layer determines how many steps in a time series the model can predict. We can design neural network architectures with multiple outputs in time-series forecasting to forecast more … Read more
This tutorial develops a Twitter bot in Python that will generate automated trading signals. The bot will pull real-time price data on various cryptocurrencies (Bitcoin, Ethereum, Doge, etc.) from the crypto exchange Gate.io and analyze it using predefined rules. Whenever … Read more
You can do various things with cryptocurrency price data, such as creating your own forecasting models, illustrating historical prices, or performing chart analysis. But first, you need to get hold of the data. Nowadays, several APIs provide access to cryptocurrency … Read more
ARIMA (Auto-Regressive Integrated Moving Average) is a statistical modeling technique for time series analysis and forecasting. Compared to machine learning, ARIMA is a classical modeling approach that is particularly powerful when the time series being analyzed follows a clear pattern. … Read more