{"action":"created","issue":{"url":"https://api.github.com/repos/sryza/spark-timeseries/issues/12","repository_url":"https://api.github.com/repos/sryza/spark-timeseries","labels_url":"https://api.github.com/repos/sryza/spark-timeseries/issues/12/labels{/name}","comments_url":"https://api.github.com/repos/sryza/spark-timeseries/issues/12/comments","events_url":"https://api.github.com/repos/sryza/spark-timeseries/issues/12/events","html_url":"https://github.com/sryza/spark-timeseries/issues/12","id":62230049,"number":12,"title":"Create a TimeSeries-RDD","user":{"login":"kamir","id":1241122,"avatar_url":"https://avatars.githubusercontent.com/u/1241122?v=3","gravatar_id":"","url":"https://api.github.com/users/kamir","html_url":"https://github.com/kamir","followers_url":"https://api.github.com/users/kamir/followers","following_url":"https://api.github.com/users/kamir/following{/other_user}","gists_url":"https://api.github.com/users/kamir/gists{/gist_id}","starred_url":"https://api.github.com/users/kamir/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/kamir/subscriptions","organizations_url":"https://api.github.com/users/kamir/orgs","repos_url":"https://api.github.com/users/kamir/repos","events_url":"https://api.github.com/users/kamir/events{/privacy}","received_events_url":"https://api.github.com/users/kamir/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":12,"created_at":"2015-03-16T22:55:24Z","updated_at":"2016-08-22T09:33:58Z","closed_at":"2015-04-17T17:36:10Z","body":"Based on the idea of the TimeSeries Bucket, I suggest to define a \"TimeSeriesRDD\" for which several core functions are well defined, such as smoothing, filtering, \"filling the gap\", FFT, etc.  The TimeSeriesRDD would help to abstract the storage details away and offers ways to project, aggregate or even expand the dataset.\r\n\r\nIn some cases we need event data, in other cases the spectrum is required in contextual normalization using the Time Resolved Relevance Index is an example for integration of additional structural information into the time series analysis procedure. Data wrangling is often not trivial. Because of this, it seems to be useful if a set of primitive transformations is already available as part of a specialized RDD."},"comment":{"url":"https://api.github.com/repos/sryza/spark-timeseries/issues/comments/241360854","html_url":"https://github.com/sryza/spark-timeseries/issues/12#issuecomment-241360854","issue_url":"https://api.github.com/repos/sryza/spark-timeseries/issues/12","id":241360854,"user":{"login":"Umair044","id":20968663,"avatar_url":"https://avatars.githubusercontent.com/u/20968663?v=3","gravatar_id":"","url":"https://api.github.com/users/Umair044","html_url":"https://github.com/Umair044","followers_url":"https://api.github.com/users/Umair044/followers","following_url":"https://api.github.com/users/Umair044/following{/other_user}","gists_url":"https://api.github.com/users/Umair044/gists{/gist_id}","starred_url":"https://api.github.com/users/Umair044/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Umair044/subscriptions","organizations_url":"https://api.github.com/users/Umair044/orgs","repos_url":"https://api.github.com/users/Umair044/repos","events_url":"https://api.github.com/users/Umair044/events{/privacy}","received_events_url":"https://api.github.com/users/Umair044/received_events","type":"User","site_admin":false},"created_at":"2016-08-22T09:33:58Z","updated_at":"2016-08-22T09:33:58Z","body":"@sryza for your code what version should i use for it. So that i can study more about timeSeriesRDD."}}
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