 | I created a dataset which contains forecast data which therefore can't be created retrospectively. For ~38 days, a cronjob generated daily forecasts: - 10-day horizons - ~30 predictions/day (different stocks across multiple sectors) - Fixed prompt and parameters Each run logs: - Predicted price - Natural-language rationale - Sentiment - Self-reported confidence I used stock predictions as the forecast subject, but this is not a trading system or financial advice, it's an EXPERIMENT! Even though currently I didn't find something mind-blowing, visualizing the data reveals patterns I find interesting. Currently, I just plotted trend, model bias, and ECE - more will come soon. Maybe you also find it interesting. The dataset isn't quite big, so I'm actually building a second one which is bigger with the Gemini Flash and Gemini Flash-Lite model. PS: If you are interested in the dataset or the MVP with a dashboard to crawl data quickly, just mention it in the comments. [link] [comments] |
Digging through 38 days of live AI forecast data to find the unexpected
Reddit r/artificial / 4/15/2026
💬 OpinionSignals & Early TrendsIdeas & Deep AnalysisTools & Practical UsageModels & Research
Key Points
- A creator built a dataset by running a daily AI forecasting cron job for ~38 days, generating about 30 stock-related predictions per day across multiple sectors using fixed prompts and parameters.
- Each forecast run logs predicted price, natural-language rationale, sentiment, and the model’s self-reported confidence, enabling analysis that couldn’t be reconstructed retrospectively.
- The author visualized the results to look for patterns such as trend behavior, model bias, and calibration metrics (ECE), while noting no major “mind-blowing” findings yet.
- Because the initial dataset is small, they are expanding it into a second, larger dataset using Gemini Flash and Gemini Flash-Lite, with an optional dashboard/MVP to crawl and review data quickly.
- The post frames the work explicitly as an experiment rather than a trading system or financial advice, inviting others to request or collaborate on the dataset/dashboard.
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