taogeanton2

taogeanton2's Stars

• GrowingGit/GitHub-Chinese-Top-Charts

  :cn: GitHub中文排行榜，各语言分设「软件 | 资料」榜单，精准定位中文好项目。各取所需，高效学习。

  Language:Java99.5k2.6k33913.1k

• jindongwang/transferlearning

  Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

  Language:Python13.6k3403423.8k

• unit8co/darts

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

  Language:Python8.2k641.6k897

• facebookresearch/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.

  Language:Python5.5k79188562

• timeseriesAI/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

  Language:Jupyter Notebook5.4k66662665

• online-ml/river

  🌊 Online machine learning in Python

  Language:Python5.1k85610555

• mljar/mljar-supervised

  Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

  Language:Python3.1k50663411

• MaxBenChrist/awesome_time_series_in_python

  This curated list contains python packages for time series analysis

  2.1k8215366

• linkedin/greykite

  A flexible, intuitive and fast forecasting library

  Language:Python1.8k39106106

• jina-ai/finetuner

  :dart: Task-oriented embedding tuning for BERT, CLIP, etc.

  Language:Python1.5k2825267

• AutoViML/Auto_TS

  Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

  Language:Jupyter Notebook7452190115

• dmbee/seglearn

  Python module for machine learning time series:

  Language:Python574262963

• 4paradigm/AutoX

  AutoX is an efficient automl tool, which is mainly aimed at data mining tasks with tabular data.

  Language:Jupyter Notebook5021344131

• yuxiaowww/BDCI-2018-Supply-Chain-Demand-Forecast

  初赛Rank1 复赛Rank1 2018 CCF 大数据与计算智能大赛 供应链需求预测 Miracccccccle

  Language:Python17510176

• mapr-demos/predictive-maintenance

  Demonstration of MapR for Industrial IoT

  Language:Jupyter Notebook11320360

• online-ml/chantilly

  🍦 Deployment tool for online machine learning models

  Language:Python97112317

• luoda888/CCF2018-Top2-Demand-Forecast

  Solutions of the forecast problem using Xgboost

  Language:Python914426

• matthiasanderer/m5-accuracy-competition

  My second place solution in the M5 Accuracy competition

  Language:Jupyter Notebook694123

• songxxiao/m5_compete

  Sliver Solution (Top 2%) for Kaggle M5 Forecasting competition

  Language:Jupyter Notebook422211

• KrishnanSG/pytsal

  An easy to use low-code open-source python framework for Time Series analysis, visualization, forecasting along with AutoTS

  Language:Python40506

• MaxHalford/creme

  One of the ancestors of River

  Language:Python33401

• SohitKalluru/Forecasting-of-order-demand-in-warehouses-using-autoregressive-integrated-moving-average.

  Forecasting of order demand in warehouses using auto-regressive integrated moving average(ARIMA).

  Language:Jupyter Notebook241011

• ap-atul/wavelets

  A simple and easy implementation of Wavelet Transform

  Language:Python22304

• VXenomac/100-pandas-puzzles-cn

  Pandas 循序渐进一百题（60% 已完成）

  Language:Jupyter Notebook18105

• gumption/pydata-simpsons

  Content associated with a PyData Seattle 2017 tutorial on Unevenly spaced time series analysis of The Simpsons using pandas

  Language:HTML153013

• YORK-CHAN/Rank1-Crawler-weather---of-BDCI-2018-Supply-Chain-Demand-Forecast

  2018 BDCI供应链需求预测复赛Rank1的天气爬虫代码

  Language:Python13004

• dutta33/Retail-Demand-Forecasting-Model-using-Factorization-Machines

  It is challenging to build useful forecasts for sparse demand products. If the forecast is lower than the actual demand, it can lead to poor assortment and replenishment decisions, and customers will not be able to get the products they want when they need them. If the forecast is higher than the actual demand, the unsold products will occupy inventory shelves, and if the products are perishable, they will have to be liquidated at low costs to prevent spoilage. The overall objective of the model is to use the retail data which provides us with historic sales across various countries and products for a firm. We use this information given, and make use of FM’ s to predict the sparse demand with missing transactions. The above step then enhances the overall demand forecast achieved with LSTM analysis. As part of the this project we answered the following questions: How well does matrix factorization perform at predicting intermittent demand How does matrix factorization approach improve the overall time-series forecasting

  Language:Jupyter Notebook7303

• ArimaKausik/Vehicular_Emission_Model

  Machine learning framework to optimize emissions of a Volvo diesel engine

  Language:Python1104

• taogeanton2/BDCI-2018-Supply-Chain-Demand-Forecast

  初赛Rank1 复赛Rank1 2018 CCF 大数据与计算智能大赛 供应链需求预测 Miracccccccle

  Language:Python1000

• taogeanton2/demand_forecast

  Language:Python1100