Uploading data

RINK accepts comma-separated values (.csv) files up to 10 MB. There's no minimum row count for upload, but you'll need at least 30 rows of valid numeric data to train a model.

File requirements

Requirement	Value
Format	.csv (UTF-8 recommended)
Size	≤ 10 MB
Header row	Required — first row is treated as column names
At least one	Numeric column
Recommended	A date / timestamp column

If a non-CSV file is selected, the upload zone rejects it client-side before any bytes leave your browser. The same check is repeated on the gateway and the ML service.

Date column detection

When you upload, RINK looks for a date column in this order:

1. Named columns: date, timestamp, time, datetime, ds, period — case-insensitive variants like Date, DATE are also matched.
2. Parseable strings: any object-typed column where ≥60% of the first 20 sampled values parse as a date.

If a date column is found, RINK:

• Sorts the rows ascending by date. Reverse-chronological CSVs are reordered, so "most recent" always means the last row after upload.
• Infers the cadence from the median delta between consecutive timestamps. Buckets: daily / weekly / monthly / quarterly / yearly, or every X days for irregular series.

If no date column is found, rows are processed in the order they appear in the file and the cadence shows as unknown. You can still train and forecast, but step semantics fall back to "+1, +2, +3, …" instead of real future dates.

Numeric column detection

After excluding the date column, RINK collects all numeric columns. The first match wins from a preference list:

value, y, target, close, price, pmms30

If none of these are present, the first numeric column in CSV order is used.

You can override the auto-detection at any time using the column picker in the Model card — see Switching columns below.

What happens after upload

1. The file is forwarded to the ML service (which streams it to disk under /var/data/users/<your_uuid>/uploaded.csv).
2. The model auto-trains. Status toast: "Trained on N rows · weekly cadence · RMSE … · MAE …"
3. The chart and KPIs refresh. The prediction input pre-fills with the most recent N values.

Subsequent re-uploads replace the previous file and model.

Switching columns

If your CSV has multiple numeric columns, the Model card shows them all:

• 2–6 columns — emerald pill buttons.
• 7+ columns — a <select> dropdown.

Picking a different column atomically:

1. Refetches /api/data?column=NAME.
2. Calls /api/train with { "column": "NAME" }.
3. Clears stale predictions.

The selection is saved to localStorage so it persists across reloads. Uploading a new CSV clears the saved choice.

Storage and cleanup

• Each user's data is isolated under /var/data/users/<user_id>/ — other users cannot see or access your data.
• Files are deleted automatically when you sign out (manual or after the 4-hour idle timeout).
• There is currently no batch-export feature; copy your forecast values out of the workspace if you want them after sign-out.

Limits

Property	Limit
File size	10 MB
Server-stored data per user	One CSV at a time (replaces on re-upload)
Min rows to train	30 (after feature dropping)
Concurrent users on Render Starter	~50 lightly active