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Top 12 Jupyter Notebook Forecasting Projects
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DeepLearningExamples
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
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tsai
Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
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covid19-severity-prediction
Extensive and accessible COVID-19 data + forecasting for counties and hospitals. 📈
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GAN-RNN_Timeseries-imputation
Recurrent GAN for imputation of time series data. Implemented in TensorFlow 2 on Wikipedia Web Traffic Forecast dataset from Kaggle.
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Deep_XF
Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
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SaaSHub
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DataDrivenDynSyst
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
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finite-element-networks
Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks" at ICLR 2022
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lockdowndates
Retrieve the dates of the restrictions imposed by governments in countries around the world during the covid-19 pandemic.
Project mention: Aeon: A unified framework for machine learning with time series | news.ycombinator.com | 2023-06-22Also https://github.com/timeseriesAI/tsai
I do not have a horse in the race, but it is interesting to see open source comparisons to traditional timeseries strategies: https://github.com/Nixtla/nixtla/tree/main/experiments/amazo...
In general, the M-Competitions (https://forecasters.org/resources/time-series-data/), the olympics of timeseries forecasting, have proven frustrating for ML methods... linear models do shockingly well and the ML models that have won, generally seem to be variants of older tree-based methods (ie. LightGBM is a favorite).
Will be interesting to see whether the Transformer architecture ends up making real progress here.
Project mention: Moirai: A Time Series Foundation Model for Universal Forecasting | news.ycombinator.com | 2024-03-25Code is available! https://github.com/SalesforceAIResearch/uni2ts
Project mention: We're building PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series | /r/learnprogramming | 2023-06-19Due to all kinds of reasons like failures of collection sensors, communication errors, and unexpected malfunctions, missing values are common to see in time series from the real-world environment. No matter whether we like them or not, missing data makes partially-observed time series (POTS) a pervasive problem in open-world modeling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated toolkit. PyPOTS is created to fill in this gap. PyPOTS (pronounced "Pie Pots") is the first (and so far the only) Python toolbox/library specifically designed for data mining and machine learning on partially-observed time series (POTS), namely, incomplete time series with missing values, A.K.A. irregularly-sampled time series, supporting tasks of imputation, classification, clustering, and forecasting on POTS datasets. It is born to become a handy toolbox that is going to make data mining on POTS easy rather than tedious, to help engineers and researchers focus more on the core problems in their hands rather than on how to deal with the missing parts in their data. PyPOTS will keep integrating classical and the latest state-of-the-art data mining algorithms for partially-observed multivariate time series. For sure, besides various algorithms, PyPOTS has unified APIs together with detailed documentation and interactive examples across algorithms as tutorials. Feedback, questions, and contributions are all very welcome! Website: https://pypots.com Paper link: https://arxiv.org/abs/2305.18811 GitHub repo: https://github.com/WenjieDu/PyPOTS Tutorials: https://github.com/WenjieDu/BrewPOTS Docs: https://docs.pypots.com
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Jupyter Notebook Forecasting related posts
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Deep_XF: NEW Data - star count:100.0
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Deep_XF: NEW Data - star count:100.0
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We're building PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series
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GAN-RNN_Timeseries-imputation: NEW Data - star count:133.0
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GAN-RNN_Timeseries-imputation: NEW Data - star count:133.0
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[Discussion] Amazon's AutoML vs. open source statistical methods
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Statistical methods outperform Amazon’s ML Forecast
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A note from our sponsor - InfluxDB
www.influxdata.com | 4 May 2024
Index
What are some of the best open-source Forecasting projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | DeepLearningExamples | 12,642 |
| 2 | tsai | 4,703 |
| 3 | nixtla | 1,429 |
| 4 | uni2ts | 416 |
| 5 | covid19-severity-prediction | 227 |
| 6 | GAN-RNN_Timeseries-imputation | 164 |
| 7 | Deep_XF | 110 |
| 8 | DataDrivenDynSyst | 62 |
| 9 | finite-element-networks | 60 |
| 10 | BrewPOTS | 39 |
| 11 | lockdowndates | 6 |
| 12 | wb_gdp_predict | 6 |
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