SN91, Cascade
@cascade_sn91
Training time series foundation models
On Bittensor Subnet 91
By @tensorlink_dev
27 Following 296 Followers
Six models. Three forecasting modes. One API.
Ephemeris is live: time series foundation models from Amazon, Google, Datadog - and soon from #SN91# itself.
Submit a series ➡️ Receive a forecast. Try it now:
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Introducing multivariate scoring on Cascade.
Real-world problems are rarely one line. They move together. #SN91# can now evaluate related time series collectively, producing scores that better reflect the structure of real forecasting problems.
Multiple forecast channels are averaged into a single mean, resulting in one GIFT-Eval-weighted contribution. This preserves the value of multivariate data without allowing problems with more channels to carry disproportionate weight.
Multivariate scoring is now live on mainnet.
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Cascade just changed how miners compete.
🔥 No more heat screen. Every challenger trains at full budget, so nobody gets cut before they’ve had a real shot.
🔭 Scoring now spans three horizons: 64, 256 and 720 blocks, pushing miners to optimise across short, medium and long-range forecasts.
👑 A 1% edge can now dethrone the king, intensifying the competition.
1️⃣ One entry per generator, rewarding genuine model diversity, not the same model with different settings.
#SN91#
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Cascade is building the full stack for time-series intelligence. Not just pretraining models.
1️⃣ SN91 runs the research competition.
2️⃣ TSBench-Forge provides the evidence
3️⃣ Ephemeris provides model access and routing
4️⃣ Gnomon brings that capability into agent workflows
Over the next few weeks, we’ll begin opening access to our benchmark, the API for time series models (including Cascade's model), and the agentic harness.
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