Time series forecasting with machine learning models

Lightning ⚡️ fast forecasting with statistical and econometric models.

Predict future energy consumption based on historical data.Create a model that predicts energy consumption in households or businesses to optimize energy distribution and reduce costs.Assist energy companies in planning and managing supply efficiently.

Multi algorithm stock predictor built using Streamlit

ARIMA time series implementation in PyTorch with optional support for Bayesian priors.

e-Portfolio showcasing my personal projects.

chatbot designed to allow users to interact with transformer models

A DSS leveraging CPI data provides real-time and predictive insights for MoSPI, NSO, and CSO. Using ARIMA, SARIMA, and LSTM models, it forecasts CPI trends across sectors, supports decision-making, and features Streamlit-based visuals and a feedback loop to improve inflation management and economic stability.

November 2021 - Topics In Finance - Discovering break points with independent forecasts using transgenic forms of time series data developed with ARIMA models

Stock price predictor using time series models

Forecasting the revenue of a private company and predicted the future revenue

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

A Novel Hybridized Forecasting Technique Utilizing ARIMA and Large Language Models

This project aims to predict monthly bike trips in Montreal for 2024 based on historical data from 2019-2023.

Web App Forecasting Top 5 Crypto Use Triple Exponential Smoothing and ARIMA

A risk analysis 🔒 on Maven dependency trees 🌳 using a combination of risk prediction methods for a Masters practicum project.

An easy-to-use Hi-C data processing software supporting distributed computation.

Cryptocurrency automatic trading

time series analysis using streamlit

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.