Popular GitHub Repositories for Ngboost Forecast

NeuralProphet: A simple forecasting package

[ICLR 2024] Official implementation of " ? Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

Natural Gradient Boosting for Probabilistic Prediction

RNN based Time-series Anomaly detector model implemented in Pytorch.

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

tfts: Time Series Deep Learning Models in TensorFlow

Modeltime unlocks time series forecast models and machine learning in one framework

Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

? A Collection of Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)

AI-powered inventory management system using LSTM models and transfer learning to forecast product demand, minimize stockouts, and reduce overstock. Designed for easy adaptation to real-world business data.

This repository containts materials for End-to-End AI for Science

foot是一个集足球数据采集器,简单分析的项目.AI足球球探为程序全自动处理,全程无人为参与干预足球分析足球预测程序.程序根据各大指数多维度数据,结合作者多年足球分析经验,精雕细琢,集天地之灵气,汲日月之精华,历时七七四十九天,经Bug九九八十一个,编码而成.有兴趣的朋友,可以关注一下公众号AI球探(微信号ai00268).

Recurrent GAN for imputation of time series data. Implemented in TensorFlow 2 on Wikipedia Web Traffic Forecast dataset from Kaggle.

Time Series Forecast with Bitcoin value, to detect upward/down trends with Machine Learning Algorithms

BERT, AWS RDS, AWS Forecast, EMR Spark Cluster, Hive, Serverless, Google Assistant + Raspberry Pi, Infrared, Google Cloud Platform Natural Language, Anomaly detection, Tensorflow, Mathematics

An R package with Python support for multi-step-ahead forecasting with machine learning and deep learning algorithms

Python Voice Assistant project can: recognize and synthesize speech without Internet access; report the weather forecast anywhere in the world; make a search in the Google; make a video search in the YouTube; make a search for a definition in Wikipedia and read it; translate from the target language to the user's native language, and much more

implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)

Using Deep Learning for Demand Forecasting with Amazon SageMaker

Time series based anomaly detector