{"id":257,"date":"2016-06-08T16:45:14","date_gmt":"2016-06-08T13:45:14","guid":{"rendered":"http:\/\/grechka.family\/dmitry\/blog\/?p=257"},"modified":"2016-06-09T00:34:41","modified_gmt":"2016-06-08T21:34:41","slug":"solar-wind-observations-epam-ace","status":"publish","type":"post","link":"https:\/\/grechka.family\/dmitry\/blog\/2016\/06\/solar-wind-observations-epam-ace\/","title":{"rendered":"Solar wind observations by EPAM ACE"},"content":{"rendered":"

This is a part of study described in the dedicated post<\/a>.<\/p>\n

We have per minute solar wind observations (density, temperature and velocity) recorded by EPAM instrument<\/a> of Advanced Composition Explorer (ACE) spacecraft<\/a>. (see this link<\/a> for data files)<\/p>\n

This data from ACE can be used for two purposes. First, we can use this particular archive of observations (e.g. 2015 year) to fit the prediction model parameters. Second, we can use very recent measurements coming from ACE as predictors for forecasting the wind velocity for near future.
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\nHaving per minute measurements are too fine grained. Using solar wind observations in such form brings the noise to the machine learning algorithms which is bad.
\nI want to aggregate the data hourly. For each hour I want to get separate \u201ctrend\u201d value and noise level for each of three data variables (density, temperature, velocity).<\/p>\n

Thus we will get<\/p>\n