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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: Bayway/italian-open-timeseries
Bologna: 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.
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: Bayway/italian-open-timeseries
Bologna: 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.
