The derivation window defines the rolling window in creating lagged features: https://docs.datarobot.com/en/docs/data/transform-data/feature-discovery/fd-time.html#prediction-poi...
The model is not limited to this window to detect seasonality: https://docs.datarobot.com/en/docs/modeling/time/ts-reference/ts-feature-lists.html#intra-month-seas...
So having too long a derivation window is actually the concern that you mentioned for identifying seasonality:
- Longer FDW means less training data: with a 150+ day FDW, we'd lose almost half of the 1 year history for the model to learn the trend;
- When you select longer FDWs (say, 3 months), DataRobot will automatically derive and test shorter derivation windows (say one week or one month). So 150+ day FDW could take extended time to run.
You can use the API client to build a model factory and test various FDWs. But please align the backtesting periods and forecast windows for an apples to apples comparison. Also, since the data is only 1 year in history, it might be worthwhile to avoid using the month as a feature.