Answers.Body: <p>If you use the 15-minute dataset without aggregation to hourly, the results will be at a higher resolution, and therefore potentially more granular or noisy.</p>
  <p>Aggregating to hourly data will give you a smoother and less noisy view that might miss some finer details. You should determine what kind of information you want to retain and how best to summarize the data to an hourly level such as mean, sum, max/min, etc. The aggregation method can introduce bias if not chosen carefully. For example, using the mean for non-normal data or highly skewed data could misrepresent the hourly values.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-09-02T04:30:35.610Z
Answers.LastActivityDate: 2024-09-02T04:30:35.610Z
Answers.UserDisplayName: cinch
Body: <p>Suppose I have one dataset which every point (means every row) is per 15 minutes, but my requirements is time resolution of an hour.</p>
  <p>If I use the dataset which is time resolution of 15 minutes, would I get same result as of an hour? Or how to deal with this situation?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-09-15T08:33:28.483Z
Comments.Text: Cross-posted: https://ai.stackexchange.com/q/46619/1794, https://datascience.stackexchange.com/q/130155/8560, https://stats.stackexchange.com/q/653560/2921.  Please [do not post the same question on multiple sites](https://meta.stackexchange.com/q/64068).
Comments.UserDisplayName: D.W.
Tags: <datasets><data-preprocessing><data-science>
Title: Time resolution with dataset
UserDisplayName: the_tomato
ThreadText: # Time resolution with dataset
  
  (Asked by: username_0 on 2024-08-29)
  
  Suppose I have one dataset which every point (means every row) is per 15 minutes, but my requirements is time resolution of an hour.
  
  If I use the dataset which is time resolution of 15 minutes, would I get same result as of an hour? Or how to deal with this situation?
  
  **Tags:** datasets, data-preprocessing, data-science
  > **Comments:**
  > **username_1**: Cross-posted: https://ai.stackexchange.com/q/46619/1794, https://datascience.stackexchange.com/q/130155/8560, https://stats.stackexchange.com/q/653560/2921.  Please [do not post the same question on multiple sites](https://meta.stackexchange.com/q/64068).  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2024-09-02. Score: 1
  
  If you use the 15-minute dataset without aggregation to hourly, the results will be at a higher resolution, and therefore potentially more granular or noisy.
  
  Aggregating to hourly data will give you a smoother and less noisy view that might miss some finer details. You should determine what kind of information you want to retain and how best to summarize the data to an hourly level such as mean, sum, max/min, etc. The aggregation method can introduce bias if not chosen carefully. For example, using the mean for non-normal data or highly skewed data could misrepresent the hourly values.
  
  
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