# Need clarity about the 'Timestamp' **URL:** \ **Category:** Cold Start Energy Forecasting\ **Created:** [October 14, 2018, 7:42pm UTC](https://community.drivendata.org/t/need-clarity-about-the-timestamp/2800 "2018-10-14T19:42:11Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![LastRocky](https://avatars.discourse-cdn.com/v4/letter/l/5f9b8f/32.png) [@LastRocky](https://community.drivendata.org/u/LastRocky)\ **Post date:** [October 14, 2018, 7:42pm UTC](https://community.drivendata.org/t/need-clarity-about-the-timestamp/2800/1 "2018-10-14T19:42:11Z")
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. ---
**Author:** ![ironbar](https://yyz2.discourse-cdn.com/flex028/user_avatar/community.drivendata.org/ironbar/32/671_2.png) [@ironbar](https://community.drivendata.org/u/ironbar)\ **Post date:** [October 15, 2018, 9:25am UTC](https://community.drivendata.org/t/need-clarity-about-the-timestamp/2800/2 "2018-10-15T09:25:45Z")
I think it means start point, not end point. ---
**Author:** ![LastRocky](https://avatars.discourse-cdn.com/v4/letter/l/5f9b8f/32.png) [@LastRocky](https://community.drivendata.org/u/LastRocky)\ **Post date:** [October 15, 2018, 9:42am UTC](https://community.drivendata.org/t/need-clarity-about-the-timestamp/2800/3 "2018-10-15T09:42:24Z")
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… ---
**Author:** ![c3josh](https://avatars.discourse-cdn.com/v4/letter/c/c89c15/32.png) [@c3josh](https://community.drivendata.org/u/c3josh)\ **Post date:** [October 15, 2018, 4:07pm UTC](https://community.drivendata.org/t/need-clarity-about-the-timestamp/2800/4 "2018-10-15T16:07:11Z")
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. [https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.resample.html) see closed and label kwargs ---
**Author:** ![bull](https://yyz2.discourse-cdn.com/flex028/user_avatar/community.drivendata.org/bull/32/8_2.png) [@bull](https://community.drivendata.org/u/bull)\ **Post date:** [October 17, 2018, 5:11pm UTC](https://community.drivendata.org/t/need-clarity-about-the-timestamp/2800/5 "2018-10-17T17:11:10Z")
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!