AlexXiaobai

AutoTS

Automated Time Series Forecasting

flow-forecast

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

tsai

Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai

pytorch-forecasting

Time series forecasting with PyTorch

gluonts

Probabilistic time series modeling in Python

Kats

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

darts

A python library for user-friendly forecasting and anomaly detection on time series.

tsfresh

Automatic extraction of relevant features from time series:

Time-Series-Library

A Library for Advanced Deep Time Series Models.

Ebook-An_introduction_to_robust_optimization

EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

causalml

Uplift modeling and causal inference with machine learning algorithms

sktime

A unified framework for machine learning with time series

awesome-causality-algorithms

An index of algorithms for learning causality with data

ordpy

A Python package for data analysis with permutation entropy and ordinal network methods.

antropy

AntroPy: entropy and complexity of (EEG) time-series in Python

pyEntropy

Entropy for Python

DoubleMachineLearning

R codes for double machine learning proposed by Chernozhukov et al. (2018, Econometrics Journal)

grf

Generalized Random Forests

SOFTX-D-22-00286

Uplift modeling and causal inference with machine learning algorithms

did_multiplegt_dyn

|| Stata | R || Estimation of event-study Difference-in-Difference (DID) estimators in designs with multiple groups and periods, and with a potentially non-binary treatment that may increase or decrease multiple times.

missing_data

Imputing missing stock anomalies data with EM implementation

TradeMaster

TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow:

PlatEMO

Evolutionary multi-objective optimization platform

mealpy

A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)

EvoloPy

EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.

DCIC2024-PhotoVoltaic

本赛题要求选手基于历史光伏发电数据、天气数据、光伏设备空间相对位置等信息，通过建立适当的模型，对未来一段时间内的光伏发电出力进行预测。A榜使用外部数据得分0.88501103804 排名32，未使用外部数据得分0.88042407737 ；B榜得分0.90467829011排名21.

Industrial-Algorithm-Competition

全国工业大数据算法大赛，省赛二等奖解决方案，已经过实际生产线验证。包括视觉检测模型和动态误差补偿模型。