tablespoon
lambdo
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tablespoon | lambdo | |
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9 | 3 | |
39 | 22 | |
- | - | |
5.3 | 0.0 | |
6 months ago | over 3 years ago | |
Python | Python | |
MIT 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.
tablespoon
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Statistical vs. Deep Learning forecasting methods
I use my package https://github.com/alexhallam/tablespoon to generate naive forecasts then evaluate the crps of the naive vs the crps of the alternative method. This “skill score” approach is very good.
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I made a library that makes naive forecasting easy
Source code is here tablespoon
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[P] Time-series Benchmark methods that are Simple and Probabilistic
tablespoon makes generating these naive methods easy while taking advantage of Stan's efficient No U-Turn Sampler - much the same way Facebook Prophet it built on top of Stan.
- [P] tablespoon: Time-series Benchmark methods that are Simple and Probabilistic
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- GitHub - alexhallam/tablespoon: 🥄✨Time-series Benchmark methods that are Simple and Probabilistic
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✨Announcing development of a benchmark forecasting library to be used as alongside AI forecasting methods. ✨
I just started the development of tablespoon. The purpose of this package is to make time-series benchmark forecasts that are simple and probabilistic.
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Time-series Benchmark methods that are Simple and Probabilistic
tablespoon
lambdo
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Why isn't differential dataflow more popular?
It will return the sum of all values in column A. For large tables it will take some time to compute the result. Now assume we append a new record and want to get the new result. The traditional approach is execute this query again. A better approach is to process this new record only by adding its value in A to the result of the previous query. It is important in (stateful) stream processing.
Something similar is implemented in these libraries which however rely on a different data processing conception (alternative to map-reduce):
https://github.com/asavinov/prosto - Functions matter! No join-groupby, No map-reduce.
https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!
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Feature Processing in Go
I find this project quite interesting because sklearn has a good general design including data transformations and it does make sense to provide compatible functionality for Go.
Feature engineering in general is a hot topic and especially if features are not simple hard-coded transformations but rather can be learned from data. For example, I developed a toolkit intended for combining feature engineering and ML:
https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!
What are some alternatives?
Bayeslite - BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
Numbers-Prophecy - An experiment to demonstrate the biases and predictability of our world.
ballista - Distributed compute platform implemented in Rust, and powered by Apache Arrow.
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.
rslint - A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate
sktime - A unified framework for machine learning with time series
openHistorian - The Open Source Time-Series Data Historian
uncertainty-baselines - High-quality implementations of standard and SOTA methods on a variety of tasks.
sliding-window-aggregators - Reference implementations of sliding window aggregation algorithms
darts - A python library for user-friendly forecasting and anomaly detection on time series.
timely-dataflow - A modular implementation of timely dataflow in Rust