I once built a forecasting framework for a unicorn startup. Revenue and Pipeline predictability was the key as the company was going through the IPO phase. There were three approaches I took and 'ensemble'd them to predict the revenue and pipeline. 1. Time series based forecast based on revenue (the one OP is referring to). All the statistical time-series models come here. I primarily used H2O.ai for this. 2. Convers…
Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
71–80 of 91 posts
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#72Earlier quoted context omitted.
Can you substantiate your comment? What would you like to see in a python tool that R does uniquely well?
Not parent, but easy: https://cran.r-project.org/web/packages/forecast/index.html /e: I'm a bit out of this game for 2-3 years now, but Python had nothing comparable. Prophet is suboptimal at best. Some things are implemented here or there, but it's all over the place. So I agree that R really shines here.
This is in part why we built Darts. Now I think we can say the situation is quite different. Darts offers many things offered by the R forecast package, and then some (for instance the ability to train ML models on large datasets made of multiple potentially high-dimensional series).
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#73Lots of people have already made good library recommendations, so I will make a non-recommendation for all the data science students out there: stop thinking about libraries, and start thinking about models. "What library do I use?" is the wrong question. "What model do I use?" is the right question. Libraries are just part of the process of answering that question. That said, high quality implementations of interest…
I wouldn't mind a magic box, honestly.
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#74I once built a forecasting framework for a unicorn startup. Revenue and Pipeline predictability was the key as the company was going through the IPO phase. There were three approaches I took and 'ensemble'd them to predict the revenue and pipeline. 1. Time series based forecast based on revenue (the one OP is referring to). All the statistical time-series models come here. I primarily used H2O.ai for this. 2. Convers…
Umm this is exactly what I’m trying to build now. Any chance you’d want to connect to talk about it? juan [at] sayprimer.com
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#75Earlier quoted context omitted.
A Software Engineer. I'm just specialised a bit in DevOps, Data Engineering, and (beware buzzword) MLOps
What's MLops? Is it what I imagine? Maintaining repos of training/data, APIfying the pipeline, deploying an ML processing pipeline with CI/CD, etc?
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#76Lots of people have already made good library recommendations, so I will make a non-recommendation for all the data science students out there: stop thinking about libraries, and start thinking about models. "What library do I use?" is the wrong question. "What model do I use?" is the right question. Libraries are just part of the process of answering that question. That said, high quality implementations of interest…
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#77What about adding external variables like weather/rain to check the impact on sales? What you guys recommend?
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#78Earlier quoted context omitted.
> - 2-3 anomalies in a row > - high deviation from expected > - multiple models all detected anomaly This is basically what statistical process control charts do for you. If you haven't learned about it already, I can recommend looking it up!
Statistical process control always seemed like some thing that would benefit me in my work, but I don't know anything about it. I have looked up random Wikipedia articles, but that's all I know. Do you know of any more "serious" learning resources in that area?
I've long wanted to write an open article series for someone like you but never gotten around to it. There's so much information out there, but you sort of have to piece it together on your own, which is suboptimal.
Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#79Re: Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
#80Lots of people have already made good library recommendations, so I will make a non-recommendation for all the data science students out there: stop thinking about libraries, and start thinking about models. "What library do I use?" is the wrong question. "What model do I use?" is the right question. Libraries are just part of the process of answering that question. That said, high quality implementations of interest…
Could you suggest some good resources for learning about timeseries models?