sktime
polars
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| sktime | polars | |
|---|---|---|
| 8 | 144 | |
| 7,404 | 26,043 | |
| 2.4% | 6.1% | |
| 9.8 | 10.0 | |
| 1 day ago | 5 days ago | |
| Python | Rust | |
| BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sktime
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Keras-tuner tuning hyperparam controlling feature size
I would recommend you to read the following paper: https://arxiv.org/abs/1909.04939 and their implementation: https://github.com/hfawaz/InceptionTime . Moreover, check out sktime: https://github.com/sktime/sktime
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Does anyone know a trusted Python package for applying Croston's Time series method?
I initially used the SkTime's Croston class SKTime Croston but when I try to get the fitted values using the steps in the discussion on github, the values are the same, a straight line throughout the in-sample to ou-of-sample predictions.
- Forecasting three months ahead.
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I Need Your Help: Convincing Reasons for Python over C# for ML Pipeline?
Time series -> https://github.com/alan-turing-institute/sktime have a look and have fun :)
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Good python time series libraries?
SKTime
- Scikit-Learn Version 1.0
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Sktime: Machine Learning for Time Series
https://github.com/alan-turing-institute/sktime
It provides specialized time series algorithms and scikit-learn compatible tools to build, tune and validate time series models for multiple learning problems.
sktime is built by an active open-source community, working together during regular meetings, workshops and sprints. For new contributors, we provide mentoring sessions and tutorials.
If you are interested in contributing or just a chat about the project, feel free to submit a PR or just reach out to us. We welcome all kinds of contributions: code, API design, testing, documentation, outreach, mentoring and more.
- Darts: Non-Facebook alternative for timeseries forecasting
polars
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Why Python's Integer Division Floors (2010)
This is because 0.1 is in actuality the floating point value value 0.1000000000000000055511151231257827021181583404541015625, and thus 1 divided by it is ever so slightly smaller than 10. Nevertheless, fpround(1 / fpround(1 / 10)) = 10 exactly.
I found out about this recently because in Polars I defined a // b for floats to be (a / b).floor(), which does return 10 for this computation. Since Python's correctly-rounded division is rather expensive, I chose to stick to this (more context: https://github.com/pola-rs/polars/issues/14596#issuecomment-...).
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Polars
https://github.com/pola-rs/polars/releases/tag/py-0.19.0
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Stuff I Learned during Hanukkah of Data 2023
That turned out to be related to pola-rs/polars#11912, and this linked comment provided a deceptively simple solution - use PARSE_DECLTYPES when creating the connection:
- Polars 0.20 Released
- Segunda linguagem
- Polars: Dataframes powered by a multithreaded query engine, written in Rust
- Summing columns in remote Parquet files using DuckDB
- Polars 0.34 is released. (A query engine focussing on DataFrame front ends)
What are some alternatives?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
tslearn - The machine learning toolkit for time series analysis in Python
modin - Modin: Scale your Pandas workflows by changing a single line of code
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
arrow-datafusion - Apache DataFusion SQL Query Engine
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.
DataFrames.jl - In-memory tabular data in Julia
scikit-hts - Hierarchical Time Series Forecasting with a familiar API
datatable - A Python package for manipulating 2-dimensional tabular data structures
scikit-learn - scikit-learn: machine learning in Python
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing