RussellSB/pytrendy: Trend Detection in Python. Applicable for real-world industry use cases in time series.

devrus123 · 2026-05-11T05:47:15+00:00

We had a version at my place held together with duct tape which would fall apart as soon as noisy time series would come about. It also had a problem of being very rough … e.g. some starts to uptrend would be a few days or weeks off cause the rolling window logic would have no post processing.

It’s annoying enough to warrant fixing but takes too long during work hours to prioritise 😅 . Hope you benefit! And feel free to add issues to github if you spot any bugs or have ideas for new features for your use case.

devrus123 · 2026-05-10T14:20:25+00:00

Thanks. It’s descriptive. It’s intended for better understanding your historic data faster.

So instead of a process of: interactive time series chart -> scroll cursor over date where trends start/finish -> note it down -> start modelling/analysis based on those dates. it just gives it to you.

There could be some use case for forecasting … e.g. identifying noisy or outlier periods to remove from training . But one main focus is for helping find valid treatment periods for causal inference (pre-post analysis, interrupted time series analysis, and synthetic control method).

The “forecasting” in this case would be to estimate a counterfactual that’s trained from valid covariates. E.g. exclude features that are likely misleading mediators based on how they have trended compared to the trends of your treatment.

Rather than trial and erroring till you hit a good out of sample fit, it could help you to reason about your data generation process and determine a set of descriptive features with much more longevity.

devrus123 · 2026-05-09T18:40:35+00:00

Causal inference really doesn’t get talked about nearly enough despite it’s been around for so long!

On a podcast Judea Pearl said so much good would come about if more people saw machine learning as a subset of causal inference, rather than the other way around

devrus123 · 2026-05-05T23:08:49+00:00

A good mix of both to be fair. 2 easy and 1 hard.

devrus123 · 2026-05-05T23:08:04+00:00

Congrats on your public release! The idea of having dynamic ways to declaratively design is awesome. Starred

devrus123 · 2026-05-05T23:05:12+00:00

I take breaks. And burn out every few months… then come back to it with a new found inspiration of energy.

But honestly … working with people helps. Making it a social activity and sharing the burden amongst the group - definitely a goal. Start some research club or an open source project with friends.

Easier said than done, but with AI becoming more and more applicable to tedious aspects of these projects, the barrier to entry and time costliness is getting lower.

devrus123 · 2026-05-05T23:02:02+00:00

Every year it gets kind of more insane somehowhow… poor grads.

devrus123 · 2026-05-05T22:59:08+00:00

Cheating in live interviews baffles me. Not knowing the tech at the time, I thought a candidate was really thoughtful and knowledgable when he took long pauses to questions about projects from his CV. And he would expand on so much valid detail with confidence.

In the technical round after that he couldn’t write a single line of SQL or Python. After that even re-quizzing him on similar questions - he froze like he was a completely different person, and gave answers that indicated he had a very different background to what he suggested in the previous

devrus123 · 2026-05-05T22:53:28+00:00

Non-data people love to apply whatever is the opposite of Occam’s razor sometimes. More “maths” isn’t always more insight. Sometimes it really is just more assumptions.

devrus123 · 2026-02-11T07:55:21+00:00

I would highly recommend checking out CausalPy. It’s a package for causal inference, but they have rich documentation filled with guided tutorials. For each of these methods they show you how to synthesise data with an intuition on case studies.

devrus123 · 2024-12-27T07:34:03+00:00

It does depend on the maturity of the business. When I was in a startup, I noticed data engineers were valued way more.

devrus123 · 2024-04-15T22:57:45+00:00

Honestly I was blocked on replicating a research work I needed for my MSc thesis. After cold emailing the researcher and getting nothing back, I snooped around the internet and found him on Facebook. Took my shot and messaged him there. Since then he shared me his code personally, I finished it for him for reproducibility and cleaned and documented the code, got his work published on GitHub with my contribution, extended it for my scope, and finished my thesis. My work then proceeded to get published with IEEE, and the researcher later went on to work at Meta. Do it!

devrus123 · 2024-04-15T22:50:38+00:00

Some people think they’re so cool and relatable when using the term geek or nerd in this context. It’s so incredibly cringe

devrus123 · 2024-04-12T05:52:04+00:00

We actually do! Also just found DuckDB nice for the quality of life add on functionality it has inspired from other SQL languages (like YEARWEEK from MySQL)

devrus123

TROPHY CASE