DeepShot is a machine learning model designed to predict NBA game outcomes using advanced team statistics and rolling averages. It combines historical performance trends with contextual game data to deliver highly accurate win predictions (71%)

Thesis project about Unsupervised anomaly detection on the streaming time-series data of porfolio risk measures and returns.

MSc Finance dissertation project at Newcastle University. This project focused on forecasting the volatility of exchange rates involving the Great British Pound using EWMA, GARCH-type and Implied Volatility models.

Simple python library thats implemented EWMA, IIR and Average filter.

This repository consits of: projects and homeworks connected with research area such as Risk Management.

Discussed various methodologies that may help while implementing Time Series

Random Plotly Dash scripts, mostly TA related

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.

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.

Swift Struct implementing EWMA / Smooth Sequence of generic values

Experimental notebooks on blink detection problem by analizing it with simple thresholds, timeseries approach and a ml model.

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

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

Exponentially Weighted Probabilistic Hybrid Congestion Control for TCP

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.

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