Cisco and Splunk have launched the Cisco Time Collection Mannequin, a univariate zero shot time sequence basis mannequin designed for observability and safety metrics. It’s launched as an open weight checkpoint on Hugging Face underneath an Apache 2.0 license, and it targets forecasting workloads with out activity particular nice tuning. The mannequin extends TimesFM 2.0 with an express multiresolution structure that fuses coarse and nice historical past in a single context window.
Why observability wants multiresolution context?
Manufacturing metrics aren’t easy single scale alerts. Weekly patterns, long run progress and saturation are seen solely at coarse resolutions. Saturation occasions, visitors spikes and incident dynamics present up at 1 minute or 5 minute decision. The frequent time sequence basis fashions work at a single decision with context home windows between 512 and 4096 factors, whereas TimesFM 2.5 extends this to 16384 factors. For 1 minute knowledge this nonetheless covers at most a few weeks and sometimes much less.
This can be a drawback in observability the place knowledge platforms typically retain solely outdated knowledge in aggregated kind. Tremendous grained samples expire and survive solely as 1 hour rollups. Cisco Time Collection Mannequin is constructed for this storage sample. It treats coarse historical past as a first-class enter that improves forecasts on the nice decision. The structure operates straight on a multiresolution context as a substitute of pretending that each one inputs reside on a single grid.
Multiresolution enter and forecasting goal
Formally, the mannequin consumes a pair of contexts, (xc, xf). The coarse context (x_c) and the nice context (x_f) every have size as much as 512. The spacing of (xc) is mounted at 60 occasions the spacing of (xf). A typical observability setup makes use of 512 hours of 1 hour aggregates and 512 minutes of 1 minute values. Each sequence terminate on the identical forecast reduce level. The mannequin predicts a horizon of 128 factors on the nice decision, with a imply and a set of quantiles from 0.1 to 0.9.
Structure, TimesFM core with decision embeddings
Internally, Cisco Time Collection Mannequin reuses the TimesFM patch based mostly decoder stack. The inputs are normalized, patched into non overlapping chunks, and handed by a residual embedding block. The transformer core consists of fifty decoder solely layers. A ultimate residual block maps tokens again to the horizon. The analysis staff take away positional embeddings and as a substitute depend on patch ordering, the multiresolution construction and a brand new decision embedding to encode construction.
Two additions make the structure multiresolution conscious. A particular token, typically referred to as ST within the report, is inserted between the coarse and nice token streams. It lives in sequence area and marks the boundary between resolutions. Decision embeddings, typically referred to as RE, are added in mannequin area. One embedding vector is used for all coarse tokens and one other for all nice tokens. Ablation research within the paper present that each elements enhance high quality, particularly in lengthy context situations.
The decode process can be multiresolution. The mannequin outputs imply and quantile forecasts for the nice decision horizon. Throughout lengthy horizon decoding, newly predicted nice factors are appended to the nice context. Aggregates of those predictions replace the coarse context. This creates an autoregressive loop by which each resolutions evolve collectively throughout forecasting.
Coaching knowledge and recipe
Cisco Time Collection Mannequin is educated by continued pretraining on prime of TimesFM weights. The ultimate mannequin has 500 million parameters. Coaching makes use of AdamW for biases, norms and embeddings, and Muon for the hidden layers, with cosine studying fee schedules. The loss combines imply squared error on the imply forecast with quantile loss over the quantiles from 0.1 to 0.9. The staff trains for 20 epochs and picks the perfect checkpoint by validation loss.
The dataset is giant and skewed towards observability. The Splunk staff reviews about 400 million metrics time sequence from their very own Splunk Observability Cloud deployments, collected at 1 minute decision over 13 months and partly aggregated to five minute decision. The analysis staff states that the ultimate corpus comprises greater than 300 billion distinctive knowledge factors, with about 35 p.c 1 minute observability, 16.5 p.c 5 minute observability, 29.5 p.c GIFT Eval pretraining knowledge, 4.5 p.c Chronos datasets and 14.5 p.c artificial KernelSynth sequence.
Benchmark outcomes on observability and GIFT Eval
The analysis staff consider the mannequin on two most important benchmarks. The primary is an observability dataset derived from Splunk metrics at 1 minute and 5 minute decision. The second is a filtered model of GIFT Eval, the place datasets that leak TimesFM 2.0 coaching knowledge are eliminated.
On observability knowledge at 1 minute decision with 512 nice steps, Cisco Time Collection Mannequin utilizing a 512 multiresolution context reduces imply absolute error from 0.6265 for TimesFM 2.5 and 0.6315 for TimesFM 2.0 to 0.4788, with related enhancements in imply absolute scaled error and steady ranked chance rating. Related good points seem at 5 minute decision. Throughout each resolutions, the mannequin outperforms Chronos 2, Chronos Bolt, Toto and AutoARIMA baselines underneath the normalized metrics used within the paper.
On the filtered GIFT Eval benchmark, Cisco Time Collection Mannequin matches the bottom TimesFM 2.0 mannequin and performs competitively with TimesFM-2.5, Chronos-2 and Toto. The important thing declare isn’t common dominance however preservation of basic forecasting high quality whereas including a powerful benefit on lengthy context home windows and observability workloads.
Key Takeaways
- Cisco Time Collection Mannequin is a univariate zero shot time sequence basis mannequin that extends the TimesFM 2.0 decoder solely spine with a multiresolution structure for observability and safety metrics.
- The mannequin consumes a multiresolution context, with a rough sequence and a nice sequence, every as much as 512 steps lengthy, the place the coarse decision is 60 occasions the nice decision, and it predicts 128 nice decision steps with imply and quantile outputs.
- Cisco Time Collection Mannequin is educated on greater than 300B knowledge factors, with greater than half from observability, mixing Splunk machine knowledge, GIFT Eval, Chronos datasets and artificial KernelSynth sequence, and it has about 0.5B parameters.
- On observability benchmarks at 1 minute and 5 minute resolutions, the mannequin achieves decrease error than TimesFM 2.0’s, Chronos and different baselines, whereas retaining aggressive efficiency on the final goal GIFT Eval benchmark.
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Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its recognition amongst audiences.
