Period

Pick the date range a model trains on.

Period sets the exact date window a model trains on.

Connecting it

Drag a Period node off a Variables node (or connect a model directly to Variables to skip this step and use the full range).

The period modal with a date range picker
The period modal with a date range picker
Dragging a Period node onto the canvas

Fields

Field What it controls
Variable Mapping Which Variables mapping this window applies to. If there's only one on the canvas, it's selected for you.
Name Optional label — defaults to "Period" if left blank. Useful once a canvas has more than one period covering different windows (e.g. "Full history" vs. "Post-relaunch").
Start date / End date The window to train on. meryn pre-fills the full range of the chosen mapping's Date column; narrow it to exclude anything that would distort the fit — a launch spike, a stockout, a period before you started tracking a channel consistently.

That's all there is to fill in — Period exists so the same Variables mapping can feed more than one date window without re-uploading or re-mapping anything.

Saving is where meryn checks the window itself, because the same file can be fine over its full history and broken over a slice: a series with sales in January and nothing after is constant from July on. Some findings reject the save — a window that leaves fewer than 24 periods, a column that never changes inside the window, a paid channel with no spend at all in it. Others save anyway but are worth reading — periods missing inside the range, two channels that move almost identically over the window, a channel that's zero in most of it.

Why "editing" a Period resets what's downstream

Changing the date range always means retraining anything connected to it, so there's no in-place edit. Clicking a configured Period gives you a confirm step that tells you how many models depend on it, then reopens the modal pre-filled with the current dates so you can adjust and resubmit — the canvas wiring itself doesn't change, only what's inside the box and everything that was trained from it.

What connects here

Upstream: Variables. Downstream: EDA, or a model node — Robyn, Meridian, or PyMC.