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Continue reading →: Modeling Bitcoin Volatility Through Structural Breaks: A Compositional PerspectiveRecent advances in time series modeling have emphasized the importance of structural breaks—abrupt changes in the underlying dynamics of financial or economic data. The paper “Directional-Shift Dirichlet ARMA Models for Compositional Time Series with Structural Break Intervention” (Katz, 2026) introduces a Bayesian framework that captures these breaks using three interpretable…
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Continue reading →: Gold/Silver Ratio: GenAI with Quant Agent on Azure AI Foundry1. Introduction: The Strategic Edge of Agentic Finance In the contemporary landscape of quantitative finance, the bottleneck is no longer data availability, but the speed of insight generation. Leveraging the Microsoft AI Foundry ecosystem, we have moved beyond static scripting into the realm of Autonomous Financial Agents. This article explores…
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Continue reading →: Taming Volatility: High-Performance Forecasting of the STOXX 600 with H2O AutoMLForecasting financial markets, such as the STOXX Europe 600 Index, presents a classic Machine Learning challenge: the data is inherently noisy, non-stationary, and highly susceptible to sudden market events. To tackle this, we turn to Automated Machine Learning (AutoML)—specifically the powerful, scalable framework provided by H2O.ai and integrated into the…
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Continue reading →: Integrating Python Forecasting with R’s TidyverseIn this article, we executed a successful integration of a non-standard Python forecasting model into the R Tidyverse/Tidymodels framework, primarily leveraging the reticulate package. 1. The Objective (The Challenge) The goal was to utilize a powerful, custom Python model (nnetsauce‘s MTS wrapper around a cyb$BoosterRegressor) and integrate its outputs—predictions and…
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I’m Selcuk Disci
The DataGeeek focuses on machine learning, deep learning, and Generative AI in data science using financial data for educational and informational purposes.
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