Hi @so ,
In general, if your spikes can be explained by the variables you have provided in the input data, DataRobot models should provide predictions that will cover them accurately.
If these spikes are caused by special events, like sales or holidays, you can add a calendar file to your Time Series project. With it, DataRobot will generate special features which will help model predicting such special event spikes accurately.
As per @sam632 recommendation, spikes might be a characteristic of a certain cluster of your series. In this case, try modeling this cluster in a separate project. This way a model will have more chances to find a useful pattern in historical data to forecast such spikes.
not an expert! but the Improving Time Series Models webinar I watched last week might help you https://community.datarobot.com/t5/sessions/improving-time-series-models/ba-p/5742
this is in the attached pdf -
"Certain clusters have distinct time trends, for example only Cluster 1 (orange) has a sharp sales spike
in August each year. This is strong indication you may want to build separate projects per cluster" - fyi