[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]  Rohan Paul [@rohanpaul_ai](/creator/twitter/rohanpaul_ai) on x 73.9K followers Created: 2024-07-20 17:13:53 UTC Functime is quite cool - its a forecasting library and for your Time-series machine learning and embeddings at scale - production-ready forecasting and temporal embeddings. - time-series preprocessing (box-cox, differencing etc), cross-validation splitters (expanding and sliding window), and forecast metrics (MASE, SMAPE etc). All optimized as lazy Polars transforms ------- Temporal embeddings measure the relatedness of time-series. Embeddings are more accurate and efficient compared to statistical methods (e.g. Catch22) for characteristing time-series. Embeddings have applications across many domains from finance to IoT monitoring. They are commonly used for the following tasks: - Matching: Where time-series are ranked by similarity to a given time-series - Classification: Where time-series are grouped together by matching patterns - Clustering: Where time-series are assigned labels (e.g. normal vs irregular heart rate) - Anomaly detection: Where outliers with unexpected regime / trend changes are identified 👉 On the licensing, the crucial point to note however, the functime library is distributed under AGPL-3.0-only, which is a strong copyleft license that enforces open source on all components derived from any previous work. Means, So if you use this library, you must open-source the code under the same AGPL-3.0 license.  XXXXX engagements  **Related Topics** [functime](/topic/functime) [Post Link](https://x.com/rohanpaul_ai/status/1814710332545257817)
[GUEST ACCESS MODE: Data is scrambled or limited to provide examples. Make requests using your API key to unlock full data. Check https://lunarcrush.ai/auth for authentication information.]
Rohan Paul @rohanpaul_ai on x 73.9K followers
Created: 2024-07-20 17:13:53 UTC
Functime is quite cool - its a forecasting library and for your Time-series machine learning and embeddings at scale
production-ready forecasting and temporal embeddings.
time-series preprocessing (box-cox, differencing etc), cross-validation splitters (expanding and sliding window), and forecast metrics (MASE, SMAPE etc). All optimized as lazy Polars transforms
Temporal embeddings measure the relatedness of time-series. Embeddings are more accurate and efficient compared to statistical methods (e.g. Catch22) for characteristing time-series. Embeddings have applications across many domains from finance to IoT monitoring. They are commonly used for the following tasks:
Matching: Where time-series are ranked by similarity to a given time-series
Classification: Where time-series are grouped together by matching patterns
Clustering: Where time-series are assigned labels (e.g. normal vs irregular heart rate)
Anomaly detection: Where outliers with unexpected regime / trend changes are identified
👉 On the licensing, the crucial point to note however, the functime library is distributed under AGPL-3.0-only, which is a strong copyleft license that enforces open source on all components derived from any previous work. Means, So if you use this library, you must open-source the code under the same AGPL-3.0 license.
XXXXX engagements
Related Topics functime