Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Time-Series

Awesome list and projects of Time Series
https://github.com/ElizaLo/Time-Series

  • MIT 18.S096 Topics in Mathematics w Applications in Finance
  • Time Series Forecasting
  • Методы анализа и прогнозирования временных рядов
  • Forecasting: Principles and Practice
  • Practical Time Series Analysis: Prediction with Statistics and Machine Learning
  • Introduction to Time Series Forecasting With Python
  • Analysis of Financial Time Series
  • Economic Forecasting
  • Forecasting Economic Time Series
  • sits: Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series
  • Bayesian Time Series Models
  • Time Series Analysis
  • An overview of time series forecasting models
  • 21 Great Articles and Tutorials on Time Series
  • Introduction to the Fundamentals of Time Series Data and Analysis
  • The Complete Guide to Time Series Analysis and Forecasting
  • Making predictions on a very small time series dataset
  • Forecasting very short time series
  • Difference between **estimation** and **prediction**?
  • Is it unusual for the MEAN to outperform ARIMA?
  • Kats - to-use, and generalizable framework to perform time series analysis. Time series analysis is an essential component of Data Science and Engineering work at industry, from understanding the key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction/embedding, multivariate analysis, etc. Kats is released by Facebook's Infrastructure Data Science team. It is available for download on [PyPI](https://pypi.org/project/kats/).|
  • Prophet - linear trends are fit with seasonality and holidays. </p><p> It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts. </p><p> g<sub>t</sub> is the piecewise linear or logistic growth curve to model the non-periodic changes in the time series, s<sub>t</sub> is the seasonality term, h<sub>t</sub> is the holiday effect with irregular schedules, and εt is the error term. </p><p> On a high level, Prophet is framing the forecasting problem as a curve-fitting exercise rather than looking explicitly at the time based dependence of each observation within a time series. </p><p> As a computational tool/software, moreover, Prophet allows users to manually supply change points in fitting the trend term and set the boundaries for saturation growth, which gives great flexibility in business applications. </p><p> Prophet is open source software released by Facebook’s Core Data Science team. It is available for download on CRAN and PyPI.</p></ul>|
  • Orbit - **OR**iented **B**ayes**I**an **T**ime Series) is a general interface for **Bayesian exponential smoothing model**. The goal of Orbit development team is to create a tool that is easy to use, flexible, interitible, and high performing (fast computation). Under the hood, Orbit uses the probabilistic programming languages (PPL) including but not limited to Stan and Pyro for posterior approximation (i.e, MCMC sampling, SVI). Below is a quadrant chart to position a few time series related packages in our assessment in terms of flexibility and completeness. Orbit is the only tool that allows for easy model specification and analysis while not limiting itself to a small subset of models. For example Prophet has a complete end to end solution but only has one model type and Pyro has total specification model flexibility but does not give an end to end solution. Thus Orbit bridges the gap between business problems and statistical solutions.<ul><li>[:octocat: Orbit: A Python Package for Bayesian Forecasting](https://github.com/uber/orbit)</li><li>[Orbit’s Documentation](https://uber.github.io/orbit/)</li><li>[Quick Start](https://uber.github.io/orbit/tutorials/quick_start.html)</li><li>[Orbit: Probabilistic Forecast with Exponential Smoothing](https://arxiv.org/abs/2004.08492) Paper</li></ul>|
  • Greykite - making and insights.</p><p>The Greykite library provides a framework that makes it easy to develop a good forecast model, with exploratory data analysis, outlier/anomaly preprocessing, feature extraction and engineering, grid search, evaluation, benchmarking, and plotting. Other open source algorithms can be supported through Greykite’s interface to take advantage of this framework.</p><li>[:octocat: Greykite](https://github.com/linkedin/greykite)</li><li>[Getting Started](https://linkedin.github.io/greykite/get_started)</li><li>[A flexible forecasting model for production systems](https://arxiv.org/abs/2105.01098) Paper</li><li>[Greykite: A flexible, intuitive, and fast forecasting library](https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library)</li></ul>|
  • statsmodels - clause) license. The online documentation is hosted at statsmodels.org.|
  • Merlion - processing and post-processing layers. It has several modules to improve ease-of-use, including visualization, anomaly score calibration to improve interpetability, AutoML for hyperparameter tuning and model selection, and model ensembling. Merlion also provides a unique evaluation framework that simulates the live deployment and re-training of a model in production. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs, and benchmark them across multiple time series datasets.|
  • Auto_TS: Auto_TimeSeries
  • TensorFlow Probability
  • Pyro - ppl/pyro)</li></ul>|
  • ArviZ: Exploratory analysis of Bayesian models
  • PyStan
  • StatsForecast - time-series-forecasting-with-statsforecast-694d1670a2f3)</li><li>[]()</li><li>[]()</li></ul>|
  • Probabilistic Time Series Forecasting with 🤗 Transformers - series-transformers.ipynb#scrollTo=SxHDCa7vwPBF)</li><li>[]()</li><li>[]()</li></ul>|
  • awesome_time_series_in_python
  • Data Skeptic - [Forecasting Principles and Practice](https://podcasts.apple.com/ua/podcast/forecasting-principles-and-practice/id890348705?i=1000522928916)|
  • Seriously Social - [Forecasting the future: the science of prediction](https://podcasts.apple.com/ua/podcast/forecasting-the-future-the-science-of-prediction/id1509419418?i=1000516647970) |
  • The Curious Quant - [Forecasting COVID, time series, and why causality doesnt matter as much as you think‪](https://podcasts.apple.com/ua/podcast/ep20-prof-rob-hyndman-forecasting-covid-time-series/id1481550488?i=1000485268452)|
  • Forecasting Impact
  • The Random Sample - [Forecasting the future & the future of forecasting](https://podcasts.apple.com/ua/podcast/forecasting-the-future-the-future-of-forecasting/id1439750898?i=1000475866199) |
  • Thought Capital - [Forecasts are always wrong (but we need them anyway)](https://podcasts.apple.com/ua/podcast/forecasts-are-always-wrong-but-we-need-them-anyway/id1434491776?i=1000452853638)|
Programming Languages
Python 5 Jupyter Notebook 1 HTML 1
Keywords
python 5 time-series 3 forecasting 2 statistics 2 arima 2 machine-learning 2 prophet 2 automl 2 time-series-analysis 1 sklearn 1 python3 1 autosklearn 1 autokeras 1 auto-timeseries 1 auto-sklearn 1 auto-arima 1 r 1 tpot 1 bayesian 1 closember 1 baselines 1 data-science 1 econometrics 1 ets 1 exponential-smoothing 1 fbprophet 1 mstl 1 naive 1 neuralprophet 1 predictions 1 seasonal-naive 1 theta 1