Machine learning models are trained with historical data, but once they are used in the real world, they may become outdated and lose their accuracy over time due to a phenomenon called drift. Drift is the change over time in the statistical properties of the data that was used to train a machine...
Introducing ikNN: An Interpretable k Nearest Neighbors Model
Sequential detection of structural changes in models is a critical aspect in various domains, enabling timely and informed decision-making. This involves identifying moments when the parameters or structure of a model change, often signaling significant events or shifts in the underlying data-gen...
Outlier detection is a critical task in machine learning, particularly within unsupervised learning, where data labels are absent. The goal is to identify items in a dataset that deviate significantly from the norm. This technique is essential across numerous domains, including fraud detection, s...
This article rigorously explores the Central Limit Theorem for m-dependent random variables under sub-linear expectations, presenting new inequalities, proof outlines, and implications in modeling dependent sequences.
Principal Component Analysis (PCA) is a robust technique used for dimensionality reduction while retaining critical information in datasets. Its sensitivity makes it particularly useful for detecting outliers in multivariate datasets. Detecting outliers can provide early warnings of abnormal cond...
Overview of the Counts Outliers Detector (COD)
Albert Einstein’s quote, “Everything should be made as simple as possible, but not simpler,” encapsulates a fundamental principle in science and analytics. It emphasizes the importance of simplicity and clarity while cautioning against oversimplification that can lead to loss of essential detail ...
Outlier detection presents significant challenges, particularly in evaluating the effectiveness of outlier detection algorithms. Traditional methods of evaluation, such as those used in predictive modeling, are often inapplicable due to the lack of labeled data. This article introduces a method k...
Financial modeling plays a crucial role in the analysis and management of financial risk. Among the various models, Copula and GARCH are widely used for understanding dependencies between financial variables and modeling time series data with volatility clustering, respectively. This article expl...