Data imputation with collaborative filtering and latent factor models for wind farms time series data

The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version.

Dashboard of New House Index Pricing

Repository for replicating and comparing time series LLM models with statistical models

Creating time lagged time series data

This project enhances agricultural weather forecasting by predicting solar radiation (SRAD) using machine learning and deep learning models, including KNN, Random Forest, XGBoost, LSTM, and hybrid methods like Voting and Stacking Regressors.

CU M.Sc. Project

ISI Summer Internship

Automatically select DNNs for time series forecasting under consideration of complexity and resource consumption.

Time series forecasting using ML models (ARIMA, SARIMA, SARIMAX and Prophet)

"Power BI project analyzing and forecasting trends in Forbes' Top Billionaires, exploring wealth, industries, and geographic distributions to predict future changes."

Comprehensive Research Project as a part of internship at Seton Hill University

An LSTM price prediction model for a Brent crude algorithmic trading bot.

This repo consist of predicting Air quality values like relative humidity, absolute humidity or any other features I have used forecasting method to analyze and predict

Implemented time series forecasting to predict future orders for Glovo, utilizing models such as ARIMA/SARIMA, LASSO, XGBoost, and Linear Regression.

Exploring forecasting for energy consumption

Analyze and propose the plan to monitor and estimate business aspects

Official repo for the following paper: Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training (SCPT) (ECML PKDD DAMI '23)

Developing an accurate and reliable financial prediction model for the next 5 years using historical data, to assist investors, traders, and financial analysts make informed decisions about buying or selling stocks in a dynamic market.

Predicting future stock prices based on past close prices and sentiment provided by a series of tweets. A time series forecasting survey using sentiment analysis.