Alternatives and similar repositories for structural-time-series

Users that are interested in structural-time-series are comparing it to the libraries listed below

• amueller / aml
  Applied Machine Learning with Python
  ☆80Updated last year
• octoenergy / timeserio
  Better `keras` models for time series and beyond
  ☆61Updated last year
• exploripy / exploripy
  Pre-Modelling Analysis of the data, by doing various exploratory data analysis and Statistical Test.
  ☆51Updated last year
• tirthajyoti / Synthetic-data-gen
  Various methods for generating synthetic data for data science and ML
  ☆80Updated 3 years ago
• oegedijk / rule_estimator
  A scikit-learn compatible estimator based on business-rules with interactive dashboard included
  ☆28Updated 3 years ago
• bodywork-ml / ml-pipeline-engineering
  Best practices for engineering ML pipelines.
  ☆35Updated 3 years ago
• formlio / forml
  ForML - A development framework and MLOps platform for the lifecycle management of data science projects
  ☆107Updated 2 years ago
• NITRO-AI / NitroFE
  NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for …
  ☆106Updated 3 years ago
• microprediction / timeseries-notebooks
  Hello world univariate examples for a variety of time series packages.
  ☆55Updated 10 months ago
• jrzaurin / ml-pipeline
  Using Kafka-Python to illustrate a ML production pipeline
  ☆112Updated 2 years ago
• larsroemheld / causalinf_ex_elasticity
  A full example for causal inference on real-world retail data, for elasticity estimation
  ☆51Updated 4 years ago
• firmai / ml-fairness-framework
  FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
  ☆71Updated 3 years ago
• WillKoehrsen / mastering-large-datasets
  Work for Mastering Large Datasets with Python
  ☆19Updated 2 years ago
• ag-ds-bubble / tseuler
  A library for Time-Series exploration, analysis & modelling.
  ☆17Updated 4 years ago
• yhilpisch / dnanlp
  Python codes and Jupyter Notebooks for the Dow Jones DNA NLP applied research paper.
  ☆16Updated 6 years ago
• amueller / sklearn-one-day
  One day workshop for machine learning with scikit-learn
  ☆63Updated last year
• jphall663 / hc_ml
  Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
  ☆22Updated 5 years ago
• alteryx / categorical_encoding
  Repository for the research and implementation of categorical encoding into a Featuretools-compatible Python library
  ☆51Updated 2 years ago
• CamDavidsonPilon / PyDataNY_2019_tutorial
  Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference
  ☆25Updated 5 years ago
• carlomazzaferro / scikit-hts-examples
  Example usage of scikit-hts
  ☆57Updated 3 years ago
• ianozsvald / dtype_diet
  Tries to shrink your Pandas column dtypes with no data loss so you have more spare RAM
  ☆84Updated last year
• dushyantkhosla / ts-forecasting-ensemble
  CentOS based Docker container for Time Series Analysis and Modeling.
  ☆21Updated 5 years ago
• firmai / python-for-finance
  ☆14Updated 6 years ago
• DrDub / artfeateng
  Code for the book Art of Feature Engineering
  ☆32Updated 3 years ago
• ritchie46 / vanilla-machine-learning
  vanilla machine learning
  ☆111Updated 2 years ago
• tlentali / leab
  📈🔍 Lets Python do AB testing analysis.
  ☆78Updated 3 months ago
• kvnkho / pydata-timeseries-forecasting
  Material for PyData NYC Tutorial on Large Scale Timeseries Forecasting
  ☆27Updated 2 years ago
• deliveryhero / pyconde2019-airflow-ml-workshop
  PyConDE & PyData Berlin 2019 Airflow Workshop: Airflow for machine learning pipelines.
  ☆47Updated last year
• Spratiher9 / Sparkora
  Powerful rapid automatic EDA and feature engineering library with a very easy to use API 🌟
  ☆53Updated 3 years ago
• coiled / data-science-at-scale
  A Pythonic introduction to methods for scaling your data science and machine learning work to larger datasets and larger models, using th…
  ☆115Updated 2 years ago