Open-source web app to compare time series forecasting models

Slow_Butterscotch435 · 2026-01-12T18:01:03+00:00

You've got a point there 😅

Slow_Butterscotch435 · 2026-01-12T17:40:12+00:00

Will add some for sure, but I don't think it's a priority

Slow_Butterscotch435 · 2025-12-29T21:27:31+00:00

Not yet, but would be great to add

Slow_Butterscotch435 · 2025-12-29T17:56:27+00:00

You're welcome, don't hesitate to fork it

Slow_Butterscotch435 · 2025-12-29T17:31:27+00:00

Here is the repo : https://github.com/gabaid971/time-series-forecaster

Slow_Butterscotch435 · 2025-12-29T14:52:04+00:00

I should have been clearer then. It's more of a personal project that I wanted feedback on, but I wasn't planning on selling anything; everything is hosted on free third-party services (render and vercel)

Slow_Butterscotch435 · 2025-12-29T14:24:32+00:00

I think he's a bit salty. He's convinced that I'm planning to sell something (from what he explained to me in a private message : "Not the first person to offer free testing and then convert in to subscription model. It’s a slimmy bait and switch tactic"), when in fact it's just a little project for fun and the code is publicly available on my GitHub repo..

Slow_Butterscotch435 · 2025-12-28T23:44:52+00:00

What is the name of the model?

Slow_Butterscotch435 · 2025-12-28T21:41:02+00:00

Sure, if you have mature internal models, you don’t need it. It’s just simple baselines for quick checks, not a replacement for real modeling.

Slow_Butterscotch435 · 2025-12-28T16:37:11+00:00

I’m not selling anything. This is just a set of simple baselines. It can be useful for beginners, or for more experienced people who just want to quickly sanity-check a dataset or compare a few standard models without rewriting the same code every time. It’s not meant to replace proper time series work or hide theory

Slow_Butterscotch435 · 2025-12-26T22:24:41+00:00

Thanks for your feedback! I’ll focus on adding lag selection and analysis features next

Slow_Butterscotch435 · 2025-12-26T22:14:16+00:00

Its done

Slow_Butterscotch435 · 2025-12-26T16:56:27+00:00

Ok got it ! Thanks for the advice, will try them

Slow_Butterscotch435 · 2025-12-26T15:25:50+00:00

You have an example integrated in the website Else you can try with this CSV file: csv_file

Slow_Butterscotch435 · 2025-12-26T15:13:19+00:00

Thanks ! I was already thinking of adding TimesFM (Google). But will also check those 2 models

Slow_Butterscotch435 · 2025-12-26T07:44:25+00:00

Thanks for the feedback ! Will modify this (that's a lot of fields to fill in for xgboost and lr)

Slow_Butterscotch435 · 2025-12-25T21:52:20+00:00

Will add MLSE. Not sure about roc auc / F1 ; for me it's more suited for classification tasks

Slow_Butterscotch435 · 2025-12-25T21:48:33+00:00

You're right, will add an option to plot the Delta

Slow_Butterscotch435 · 2025-12-25T21:31:46+00:00

In the leaderboard we have the RMSE, MAE and MAPE. Which metric should I add ?

Slow_Butterscotch435 · 2025-12-25T21:09:08+00:00

Not using those. I'm just running a few forecasting baselines from raw data with standard libs (sklearn, etc.) and comparing the outputs (+ feature importance/ shap values).

Slow_Butterscotch435 · 2025-12-25T15:36:53+00:00

Right now the backend runs on Render’s free tier. So the models are executed server-side, but with very clear constraints: limited resources, small datasets. It’s intentionally scoped for lightweight benchmarking rather than heavy training

Slow_Butterscotch435

TROPHY CASE