Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

Explore Time Series functions and code snippets in this repository for efficient manipulation of time-based data. Gain insights and practical implementations of Time Series techniques to unlock the power of Time Series analysis.

Machine learning and data analysis package implemented in JavaScript and its online demo.

This repository contains a comprehensive analysis of time series data (stock prices), forecasted using various statistical and deep learning models.

running prediction models such as arima, fbprophet and neuralprophet on cpu-load datatset of server to predict trend of cpu utilisation.

A multiverse of Prophet models for timeseries

TKAN: Temporal Kolmogorov-Arnold Networks

Thesis for NTUA: Multivariate Computational Load Timeseries Forecasting & Resource Provisioning with Machine Learning Techniques

Forecasting the Adoption Process of Technology Using AI Methods

A simple and flexible code for Reservoir Computing architectures like Echo State Networks

Asset Prediction Sagemaker Pipelines Example

This repository contains analysis and predictive modeling of household electricity consumption using Python. It includes data cleaning, exploratory data analysis (EDA), time series forecasting (ARIMA, SARIMA, LSTM), and model evaluation to optimize energy usage.

Summary of Assignment Two from the Second semester of the MSc in Data Analytics program. This repository contains the CA2 assignment guidelines from the college and my submission. To see all original commits and progress, please visit the original repository using the link below.

This project involves time series analysis, examining Tesla Inc.'s (TSLA) stock performance from 2010 to 2024. Time series analysis, which studies data points collected at specific time intervals, was used to identify trends, seasonal patterns & fluctuations in Tesla’s stock prices, insights were derived from visualizations & resampling techniques.

Stock Price Forcasting Using LSTM

Temporal Kolmogorov-Arnold Transformer

The primary objective of this project is to develop a cutting-edge forecasting model utilizing advanced machine-learning algorithms and sophisticated time-series analysis techniques. The model aims to deliver precise predictions of future sales across diverse retail outlets.

Energy Forecast Benchmark Toolkit is a Python project that aims to provide common tools to benchmark forecast models.