Need clarity about the 'Timestamp'

I am confused about the real meaning of ‘Timestamp’ column in submission_format.csv. Does the ‘Timestamp’ mean ending time of a time period or? I am confused because: for example, for series_id 102781, the first shown Timestamp is “2013-03-03 00:00:00” in submission_format.csv, while the latest timestamp in cold_start_test.csv for this series_id is “2013-03-02 23:00:00” and the prediction_window is ‘daily’. Does it mean we need to predict the consumption sum from “03-02 23:00:00” to “03-03 23:00:00” (overall 24 hours) or from “03-02 00:00:00” to “03-03 00:00:00” (if the timestamp means ending time point).
If it’s the first mentioned case, then not like “hourly” case, the “Timestamp” doesn’t mean ending time point but a meaningless value. If it’s the second mentioned case, then it makes no sense, because the consumption data from “03-02 00:00:00” to “03-03 23:00:00” are already given, so we need only predict the consumption from “03-02 23:00:00” to “03-03 00:00:00” (one hour consumption), then sum up with the previous provided 23 consumptions to get the ‘daily’ consumption.

I think it means start point, not end point.

In that case, for example, for series_id 102493, the prediction_window of which is ‘Weekly’, the first timestamp shown in Submission_format.csv is “2015-10-17 00:00:00”, while the lastest timestamp shown in cold_start_test.csv is “2015-10-10 23:00:00”. So you mean the first prediction period for this series_id is from “2015-10-17 00:00:00” to “2015-10-24 00:00:00”, which I don’t think so…

FYI - I had an issue with this because of how Pandas aggregates. Weekly resolutions and above are treated as period ending. Daily and below are treated as period beginning.

see closed and label kwargs

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Thanks for bringing this up @LastRocky, and thanks for the tip @c3josh. Upon review, we can see that the weekly timestamps are actually endpoints, likely because of the issue that @c3josh mentioned. Apologies for any confusion!