3 ms·

What prediction algorithm are you using for this? Not sure if it's mentioned elsewhere on the site

• dsclough 8y ago
  I'd also be interested in hearing a bit more about this.
• amrrs 8y ago
  I 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 ago
  No, 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 ago
    How 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 ago
      Actually, 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 :-)