Cisco and Splunk have introduced the Cisco Time Series Model, a univariate zero shot time series foundation model designed for observability and security metrics. It is released as an open weight checkpoint on Hugging Face.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
* **Multiresolution data is common:** The model handles data where fine-grained (e.g., 1-minute) and coarse-grained (e.g., hourly) data coexist, a typical pattern in observability platforms where older data is often aggregated.
* **Long context windows are needed:** It's built to leverage longer historical data (up to 16384 points) than many existing time series models, improving forecasting accuracy.
* **Zero-shot forecasting is desired:** The model aims to provide accurate forecasts *without* requiring task-specific fine-tuning, making it readily applicable to a variety of time series datasets.
* **Quantile forecasting is important:** It predicts not just the mean forecast but also a range of quantiles (0.1 to 0.9), providing a measure of uncertainty.
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- TabPFN is a novel foundation model designed for small- to medium-sized tabular datasets, with up to 10,000 samples and 500 features.
- It uses a transformer-based architecture and in-context learning (ICL) to outperform traditional gradient-boosted decision trees on these datasets.
- It uses a transformer-based architecture and in-context learning (ICL) to outperform traditional gradient-boosted decision trees on these datasets.
