I had not known of Time Series (or most other) anomaly detection methods until recently, when I used several LLMs to assist with an analysis of the Florida Lott

I had not known of Time Series (or most other) anomaly detection methods until recently, when I used several LLMs to assist with an analysis of the Florida Lottery Pick4 history. For years, I'd been casually observing the daily numbers (2 draws daily for each since around ?/?/2004?, and 1 prior), which are Pick2, 3, 4, and 5, but mostly Pick4, which is 4 digits, thus has 1:10,000 odds, vs 1:100, 1:1000 and 1:100,000 for the others. With truly random numbers, it is pretty difficult to identify anything but glaring anomalies. Among some of the tests performed were: (clusters\daily\weekly; (isolated forest; (popular permutations\by date\special holidays\etc; (individual digits\deviations; (temporal frequency; (dbscan; (zscore; (patterns; (correlation; (external factors; (auto correlation by cluster; (predictive modeling; (chi squared; (Time Series ... and a few more I've forgotten. For those wondering why I'd do this, around 2023-23, the FL Lottery drastically modified their website. Previously, one could enter a number for the game of their choice and receive all historical permutations of that number over all years, going back to the 1990s. With the new modification, the permutations have been eliminated and the history only shows for 2 years. The only option for the complete history is to download the provided PDF -- however, it is full of extraneous characters and cannot be readily searched via Ctrl-F, etc. Processing this PDF involves extensive character removal to render it parsable or modestly readable. So to restore the previously functional search ability, manual work is required. The seemingly deliberate obfuscation, or obstruction, was my motivation. The perceived anomalies over the years were secondary, as I am capable of little more than speculation without proper testing. But those two factors intrigued me. Having no background in math and only feeble abilities in programming, this was a task that I could not have performed without LLMs and python code used for the tests. The test is still incomplete, having increased in complexity as I progressed and left me too tired to persist. The result were ultimately within acceptable ranges of randomness, but some patterns were present. I had made files of (all numbers that ever occurred; (all numbers that have never occurred; (popularity of single, isolated digits -- I was actually correct in my intuition here, which proved certain single were occurring with lesser or greater frequencies as would be expected; (a script to apply Optical Character Recognition on the website and append the latest results to a living text and PDF file to offer anyone interested an opportunity to freely search, parse and analyze the numbers. But I couldn't quite wangle the OCR successfully. Working with a set over 60k individual number sets, looking for anomalies over a 30 year period; if there are other methods anyone would suggest, please offer them and I might resume this abandoned project.