A subset of artificial intelligence that enables systems to learn from data and improve their performance over time without explicit programming.

A statistical technique used to predict future outcomes based on historical data, often employing machine learning algorithms.

A digital or virtual currency that uses cryptography for security, operating on decentralized networks based on blockchain technology.

The process of estimating future financial outcomes based on historical data and analytical techniques.

The systematic examination of data to extract meaningful insights and support decision-making.

The process of selecting, modifying, or creating variables (features) that improve the performance of machine learning models.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset, enhancing model reliability.

The use of computer algorithms to automate trading decisions and execute trades in financial markets.

The examination of moral principles and guidelines that govern the conduct of algorithmic trading and predictive modeling.

The process of choosing the most appropriate machine learning model for a given dataset based on performance metrics.

Quantitative measures used to evaluate the effectiveness of a predictive model, such as accuracy, precision, and recall.

The process of optimizing the parameters of a machine learning model to improve its performance.

A statistical technique used to analyze time-ordered data points to identify trends, cycles, and seasonal variations.

The assessment of public sentiment towards a particular financial asset or market, often using social media and news data.

An approach to analyze datasets to summarize their main characteristics, often using visual methods.

A statistical method used to evaluate the strength and direction of relationships between two variables.

The graphical representation of information and data to communicate insights clearly and effectively.

A subset of data used to train a machine learning model, allowing it to learn patterns and make predictions.

A separate subset of data used to evaluate the performance of a machine learning model after it has been trained.

A decentralized digital ledger that records transactions across many computers securely and transparently.

The analysis, interpretation, and manipulation of signals to extract valuable information, often used in financial data analysis.

The identification of rare items or events that differ significantly from the majority of the data, often indicating fraud or errors.

A flowchart-like structure used for decision-making, where each branch represents a possible decision or outcome.

A type of machine learning where the model is trained on labeled data, learning to predict outcomes based on input features.

A type of machine learning where the model is trained on unlabeled data, discovering patterns and structures without predefined labels.