ब्रह्मन् brahman

Estimation and forecasting of volatility using Financial Timeseries with Copulas. Includes models like GARCH, EWMA and EqWMA. Market risk management using CVaR, EVT, Risk Factors and Monte Carlo Simulation.

Codes for the design and the evaluation of the performance of control charts (Shewhart and EWMA) for a zero-inflated beta (BEZI) distribution

Code and experiments for detecting real-time shifts in correlation between time series. Includes simulation, calibration, and real-world case studies using CUSUM, Wilcoxon rank-sum sequential tests, MEWMA, and Adaptive EWMA.

EDA performed on timeseries data and different plots to get insights from data visualizations

Exponentially Weighted Probabilistic Hybrid Congestion Control for TCP

Minimal rocket health monitoring demo: Z-score, EWMA, IsolationForest on synthetic telemetry with precision/recall/F1.

Forecasting stock closing prices using statistical, deep learning, and hybrid approaches

🛰 Monitor rocket system health with point anomaly detection on time series data using simple detectors and evaluate performance metrics.

An adaptive selector for short-term forecasting of multiple time series. For each time series, it finds the best method from a pool of candidates based on their past performance.

Volatility forecasting using ARCH, GARCH, and EWMA on JPM stock

A tool to track and forecast system performance with advanced EWMA modelling

Swift Struct implementing EWMA / Smooth Sequence of generic values

Random Plotly Dash scripts, mostly TA related

NCIs Project 2024/25

This repository represents additional control charts, various plans and variables that are used within the chart scope using Minitab software

Adaptive Monitoring and Real-World Evaluation of Agentic AI Systems

Discussed various methodologies that may help while implementing Time Series

Compute an exponentially weighted mean incrementally.

An adaptive selector for short-term forecasting of multiple time series. For each time series, it finds the best method from a pool of candidates based on their past performance.