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Theoretical Physics 🇫🇷 The perceptron is not a neuron ; ElectroChemical Calculations (NMDA & AMPA receptor, apical and basal dendrite, axon initial segment, metaplasticity, ... )

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WhirlwindAI/Binance-PERP-Data-Multi-TF

liked a dataset 14 days ago

Bayway/italian-open-timeseries

reacted to Bayway's post with 🧠 14 days ago

Italian Open Time Series: 46 public Italian series for zero-shot forecasting, and a first live test of Timestral Italian data is almost absent from the corpora that time-series foundation models are trained and benchmarked on. So I collected 46 open series (CC0/CC BY): smog in Milan, bikes in Bologna, tourism, energy, earthquakes, deaths, Italian Wikipedia. Gaps stay NaN, and a tasks.yaml makes every window reproducible with fev. Seven models, zero-shot, 19 tasks (Timestral 5M to 209M, Chronos-2, TimesFM 3.0, Toto 2.0). Skill vs seasonal naive, fev-bench style: • Backtest, windows ending 14 Oct, Timestral's release day: Toto 2.0 28.4, Timestral-209M 28.2 • Live, data published after that day (6 tasks, ~1 week): Timestral-209M 32.2, Timestral-123M 30.9, TimesFM 3.0 30.3, Toto 2.0 27.7 • Monthly consumer prices of electricity and gas: plain AutoETS beats every foundation model The live round is small; I'll re-run it every week. Two surprises. Weather beats the calendar. On a Bologna bike counter, giving Timestral the observed weather of the forecast days (a perfect forecast) cut the error by 17%. Holidays and bridge days alone: 4%. Daylight saving time. The model works in UTC; cyclists don't. Across the 25 Oct clock change my first version put Monday's rush hour at 5 am, until I moved the series to Italian wall-clock time. For Bologna there's a small page: pick a street and a day in the next week for an hourly bike forecast, computed live from the city's counts and Open-Meteo, plus a map of counters and air-quality stations. Dataset: https://huggingface.co/datasets/Bayway/italian-open-timeseries Bologna: https://huggingface.co/spaces/Bayway/due-torri-due-ruote Not included: GME wholesale power prices and Terna load data. Their terms forbid redistribution, and GME bans automated extraction for any purpose, AI training included.

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PhysiQuanty/CATIE-AQ_DFP_BASE_256_VOCAB_258

Updated Jan 25 • 11 • 1