https://artaquest.org/research/Open, reproducible, AI-reviewed studies that discover seasonality and cyclical patterns in open data.enFri, 26 Jun 2026 10:00:00 GMThttps://artaquest.org/papers/topics-seasonality-model.htmlhttps://artaquest.org/papers/topics-seasonality-model.htmlArash AshrafnejadFri, 26 Jun 2026 10:00:00 GMTWe ask whether the timing of worldwide curiosity, measured by Google Trends, lines up with the positions of celestial bodies. For each of 1034 search fields - a curated atlas of skill terms (each derived from an ISCO-08 occupation, e.g. accountant -> accounting) plus learning topics - we fit 22 yearhttps://artaquest.org/papers/google-trends-recency-bias.htmlhttps://artaquest.org/papers/google-trends-recency-bias.htmlArash AshrafnejadFri, 26 Jun 2026 10:00:00 GMTGoogle Trends' most recent observations are the least reliable - sampled from incomplete data and revised later. We quantify how much recent data a time-series model should discard using a pure in-sample fitting-power criterion: crop the most recent C months of data, refit on the remainder, and reco