Hi @vfilter and thanks for writing. Our resample() method works different to pandas resample.
From our docs:

Resample creates a reindexed time series with a given frequency.
The method is used to fill holes in reindexed TimeSeries, by default 'pad'.

It is true that it would be nice to extend our method to be more like pandas` resample. I will add this to our backlog.

You can achieve the desired results with:

TimeSeries.from_dataframe(series.pd_dataframe().resample('Y').sum())

Pandas returns a *IndexResampler. We could either extend resample() or implement a new method. We could add support for IndexResampler to our stats methods like sum() which would convert it back into a TimeSeries object.

Following this issue with interest! Aggregate resample is critical to many of our workflows. Currently, we're using this function to retain timeseries metadata attributes, but it feels a bit hacky

def ts_resample(ts, freq="Y", agg="sum"):
    tmp = TimeSeries.from_dataframe(ts.pd_dataframe().resample(freq).agg(agg))
    tmp = tmp.with_static_covariates(ts.static_covariates)
    tmp = tmp.with_hierarchy(ts.hierarchy)
    return tmp