Maybe take a page from fraud detection <https://towardsdatascience.com/anomaly-fraud-detection-a-quick-overview-28641ec49ec1> and try to annotate each transaction with the hour of the day, day of the week, day of the month, and accumulated transaction volume/value when the transaction occurred. Even throw in a biweekly time period if you're feeling fancy? The best thing would be to include the location if you have it, but I don't think you do.
Sincerely, Timothy Jesionowski On Wed, Jan 24, 2024 at 4:34 PM Red S <redstre...@gmail.com> wrote: > Very interesting, thank you all. I wonder if a single user's journal would > suffice for a learning dataset in this case. For me, expenses across > categories of interest are those have been stable for years. Plus, I’m > willing to deal with false positives (but preferably not false negatives). > > There is a kind of machine learning problem called outlier detection. I > think sciki-learn library is a good starting point > > Excellent, thank you for the helpful pointers! A quick search brought up > these, which I’ve noted down to look into when I have time(TM): > https://scikit-learn.org/stable/modules/outlier_detection.html > > https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_outlier_detection.html > > > > On Wednesday, January 24, 2024 at 10:09:54 AM UTC-8 erical...@gmail.com > wrote: > > This would probably be more useful if users can provide their own examples > of abnormal and normal expenses. In that case, the model itself is > probably not very difficult; I imagine a variety of off the shelf toolkits > would work. To me, the harder part seems like making the workflow smooth > and robust -- deciding how users would flag outliers, run the classifier, > correct misclassifications, cause retraining to happen, etc. > > On Wed, Jan 24, 2024 at 10:05 AM Yichu Zhou <flyaw...@gmail.com> wrote: > > There is a kind of machine learning problem called outlier detection. I > think sciki-learn library is a good starting point if we want to use ML > techniques. But in our case, I feel the definition of “abnormal” varies on > different personal situations. It might be tricky to formulate the problem > properly. > > On Tue, Jan 23, 2024 at 21:24 Red S <redst...@gmail.com> wrote: > > Definitely! That's what I had in mind. Would you or others on this list > have experience in how to frame the problem from a deep learning > classification problem, what tools/libraries to use, and such? Pointers > appreciated. > > On Tuesday, January 23, 2024 at 8:08:31 AM UTC-8 char...@gmail.com wrote: > > Sounds like a good opportunity for deep learning classification problem. > > On Friday, January 19, 2024 at 11:45:35 AM UTC+1 Red S wrote: > > I'm curious, has anyone setup Beancount scripts or reports to flag > expenses that might need further attention? The situation that made me > think about this is a quarterly bill that doubled multiple times after > years of being stable, which is an obvious red flag. > > Unlike in the past, virtually of my Beancount interactions are highly > automated, which combined with the fact that time is at a premium these > days, causes me to miss details like this. > > In this particular case, a rule to flag expenses that deviate from their > norm over a certain time period (monthly, annually) might be simple to > write, but I was wondering for a more general, perhaps fancier solution > that would learn to distinguish what's normal and call attention to what's > not, as rules based solutions tend to be incomplete and require constant > fiddling. > > -- > You received this message because you are subscribed to the Google Groups > "Beancount" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to beancount+unsubscr...@googlegroups.com. > To view this discussion on the web visit > https://groups.google.com/d/msgid/beancount/d1cffa4f-4c32-484c-b4f8-c6d4cbf748cen%40googlegroups.com > <https://groups.google.com/d/msgid/beancount/d1cffa4f-4c32-484c-b4f8-c6d4cbf748cen%40googlegroups.com?utm_medium=email&utm_source=footer> > . > -- You received this message because you are subscribed to the Google Groups "Beancount" group. To unsubscribe from this group and stop receiving emails from it, send an email to beancount+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/beancount/CAOVsoWRStSTD1gq319FO0RfNoR4enS6SLacBHWj7E%2BKmDtkZbQ%40mail.gmail.com.
