5 ms·
I have been working in the same field, quite a few years in my own forecasting company and then later on as a side project. Not wanting to steal the thunder of
by dmichulke 8y ago
I have been working in the same field, quite a few years in my own forecasting company and then later on as a side project.
Not wanting to steal the thunder of Deepmind, but I feel should mention my site which is currently beta as in "works for a few electricity power load and supply forecasting scenarios" and quite well so. Also, it's out there, cheap and fast.
https://ausblick.cryptoport.net https://ausblick.cryptoport.net
Unfortunately, it's only in German but the basic rule is:
- upload an excel (or csv) file
- wait a minute or so (for a typical dataset of say 3 years of hourly data)
- get back the results
There are a few design rules how the excel file should look like, e.g.,
- a date/time series per sheet (it accepts most English and European formats) and as many number series as you want (always use a decimal point instead of comma here)
- one series has to have consecutive missing numbers at the end, this will be the target value
Other than that, it should just work.
Feel free to comment.
PS: The examples might be the best explanation - https://ausblick.cryptoport.net/examples https://ausblick.cryptoport.net/examples
- deleted 8y ago[deleted]
- linkingday 8y agoWhat prediction algorithm are you using for this? Not sure if it's mentioned elsewhere on the site
- dsclough 8y agoI'd also be interested in hearing a bit more about this.
- amrrs 8y agoI have previously built such a tool for retail sales forecasting (based on previous sales). The stack used R + Rshiny (web app). Method FB's `prophet` as it seemed faster and more accounting of holidays and other variations.
- dmichulke 8y agoNo, it's not mentioned. It's currently a mix of very simple techniques - a number of smart features (usually a few k) depending on the series (using lags, aggregates, curve fits, combinations of features, ...) - an iterative algorithm that selects features using maximum relevance (~ correlation with the target) / minimum redundancy and adds them to the model - simple pca and ridge regression (because it's fast) - a few optimizations of the final model (removing features, selecting a better ridge regression alpha with CV, ...) The stack is pure Clojure / Clojurescript.
- profunctor 8y agoHow did you find developing your models in clojure? I would love to switch away from python for ml but it seems to just have the ecosystem.
- dmichulke 8y agoActually, I use both but my production models are in Clojure. I often end up implementing minor things myself using lower level abstractions (e.g., Linear Regressions or PCA with whitening using Matrix libraries) and I check the results and/or try new things using scikit-learn. So in general, I'd say I do the programming (outputing intermediate CSVs, tests, web service, thread handling, UI, ...) in Clojure(Script), and try other approaches (e.g., other models/parameters/...) in Python. I'm quite happy with this pipeline but probably to some extent because I really love to understand how things work and nothing pushes you to learn as much as a missing function in your ML library :-)
- selimthegrim 8y agoYes, me as well.