# Forecasting: Weather Data Little Overlap with Train-Submit SiteId-Timestamps

**URL:** <https://community.drivendata.org/t/forecasting-weather-data-little-overlap-with-train-submit-siteid-timestamps/2242>\
**Category:** Power Laws\
**Created:** [March 23, 2018, 10:31pm UTC](https://community.drivendata.org/t/forecasting-weather-data-little-overlap-with-train-submit-siteid-timestamps/2242 "2018-03-23T22:31:07Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![ryanrunchey](https://avatars.discourse-cdn.com/v4/letter/r/bc8723/32.png) [@ryanrunchey](https://community.drivendata.org/u/ryanrunchey)\
**Post date:** [March 23, 2018, 10:31pm UTC](https://community.drivendata.org/t/forecasting-weather-data-little-overlap-with-train-submit-siteid-timestamps/2242/1 "2018-03-23T22:31:07Z")

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Please inform me if I have an outdated weather file (‘weather.csv’)… is there a fixed download somewhere?

The observed problem is that 5/6 of the ‘weather.csv’ data is out of time scope compared to the corresponding Train & Submit data.

An example:

```
Site 304 has 2 ForecastIds... which begin (train) and end (submission):

6972	2015-03-04 00:00:00 through 6973 2016-09-21 00:00:00   

However, weather data ('weather.csv') for Site 304 contains the following Timestamp '2017-12-19 21:00:00'

```

The timestamp is clearly not aligned… 5/6th of the weather data compared to Train & Submit does not overlap.

Thanks!

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**Author:** ![nathandwalker](https://yyz2.discourse-cdn.com/flex028/user_avatar/community.drivendata.org/nathandwalker/32/700_2.png) [@nathandwalker](https://community.drivendata.org/u/nathandwalker)\
**Post date:** [March 28, 2018, 2:30pm UTC](https://community.drivendata.org/t/forecasting-weather-data-little-overlap-with-train-submit-siteid-timestamps/2242/2 "2018-03-28T14:30:27Z")

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You’ll need to resample the weather timestamps and sometimes nudge them around by a few minutes (i.e., 15) to get them to match the train/submit data. There’s also often more than one weather reading per site per timestamp, so you’ll need to choose the best/closest data first (otherwise joins get really exciting, in a bad way). I got about 80% coverage after doing this and then jumped ship to work on the next steps.
