none's starred repositories
gpt-computer-assistant
Intelligence development framework in python for your product like Apple Intelligence
awesome-cpp
A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.
PayloadsAllTheThings
A list of useful payloads and bypass for Web Application Security and Pentest/CTF
Urban_Concept_Drift
[CIKM 2023] MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
pycrop-yield-prediction
A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction
Gas_Anomaly_Detection
Code of Paper D2AE: A Data Distillation Enhanced Autoencoder for Detecting Anomalous Gas Consumption
Anomaly_detection
Anomaly detection Example LSTM example with Quantile Regression Keras
Time-Series-Forecast-with-Deep-Hybrid-Architecture
Multivariate time-series forecasting with LSTNET and soft-DTW loss
Synergy-of-S1-and-S2-time-series
This repository contains the source code for fusing Sentinel-1 and Sentinel-2 time series using Multi-Output Gaussian Process Regression
Household-Power-Consumption-Forecasting
ML Time Series Analysis | SARIMA, ARMA, LSTM | Power Forecasting for Sceaux, France
-Predictive-Modeling-of-Household-Energy-Consumption-
"Explore predictive modeling of household energy use with 'SLR.ipynb'. It covers data prep, analysis, and regression techniques to forecast energy needs, focusing on environmental impacts and optimization methods."
Ensemble-Conformalized-Quantile-Regression
Valid and adaptive prediction intervals for probabilistic time series forecasting
GPR-RF-Electricity-forecast
Monthly-Electricity-forecast use GPR-RFr 某区域月电量预测,采用高斯过程回归、随机森林回归预测日电量,通过日电量累加的方式来获得月电量的预测
Gaussian-Process-Regression-Tutorial
An Intuitive Tutorial to Gaussian Processes Regression
VAE-LSTM-for-anomaly-detection
We propose a VAE-LSTM model as an unsupervised learning approach for anomaly detection in time series.
Unsupervised-Deep-Learning-Framework-for-Anomaly-Detection-in-Time-Series-
Unsupervised deep learning framework with online(MLP: prediction-based, 1 D Conv and VAE: reconstruction-based, Wavenet: prediction-based) settings for anaomaly detection in time series data