Row 6159

Row ID: 6159 | Dataset Entry | Axioma AXP Content Repository

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Hello my fellow DS/stats peeps,

I am working on a new problem where I am dealing with 15 years worth of hourly data of average website clicks. On a given day, I am interested in estimating the peak volume of clicks on a website with a 95% confidence interval. The way I am going about this is by bootstrapping my data 10,000 times for each day but I am not sure if I am doing this right or it might not even be possible.

Procedure looks as follows:

* Group all Jan 1, Jan 2,… Dec 31 into daily buckets. So I have 15 years worth of hourly data for each of these days, or 360 data points (15\*24). * For a single day bucket (take Jan 1), I sample 24 values (to mimic the 24 hour day) from the 1/1 bucket to create a resampled day, store the max during each resampling. I do this process 10,000 times for each day. * At this point, I have 10,000 bootstrapped maxes for all days of the year.

This is where I get a little lost. If I take the .975 and .025 of the 10,000 bootstrapped maxes for each day, in theory these should be my 95% bands of where the max should live. When I bootstrap my max point estimate by taking the max of the 10,000 samples, it’s the same as my upper confidence band.

Am I missing something theoretical or maybe my procedure is off? I’ve never bootstrapped a max or maybe it is not something that is even recommended/possible to do.

Thanks for taking the time to reading my post!

FieldValue
text Hello my fellow DS/stats peeps, I am working on a new problem where I am dealing with 15 years worth of hourly data of average website clicks. On a given day, I am interested in estimating the peak volume of clicks on a website with a 95% confidence interval. The way I am going about this is by bootstrapping my data 10,000 times for each day but I am not sure if I am doing this right or it might not even be possible. Procedure looks as follows: * Group all Jan 1, Jan 2,… Dec 31 into daily buc…
label r/datascience
dataType post
communityName r/datascience
datetime 2024-05-07
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Raw Record

{
  "text": "Hello my fellow DS/stats peeps,\n\nI am working on a new problem where I am dealing with 15 years worth of hourly data of average website clicks. On a given day, I am interested in estimating the peak volume of clicks on a website with a 95% confidence interval. The way I am going about this is by bootstrapping my data 10,000 times for each day but I am not sure if I am doing this right or it might not even be possible.\n\nProcedure looks as follows:\n\n* Group all Jan 1, Jan 2,… Dec 31 into daily buckets. So I have 15 years worth of hourly data for each of these days, or 360 data points (15\\*24).\n* For a single day bucket (take Jan 1), I sample 24 values (to mimic the 24 hour day) from the 1/1 bucket to create a resampled day, store the max during each resampling. I do this process 10,000 times for each day.\n   * At this point, I have 10,000 bootstrapped maxes for all days of the year.\n\nThis is where I get a little lost. If I take the .975 and .025 of the 10,000 bootstrapped maxes for each day, in theory these should be my 95% bands of where the max should live. When I bootstrap my max point estimate by taking the max of the 10,000 samples, it’s the same as my upper confidence band.\n\nAm I missing something theoretical or maybe my procedure is off? I’ve never bootstrapped a max or maybe it is not something that is even recommended/possible to do.\n\n  \nThanks for taking the time to reading my post!",
  "label": "r/datascience",
  "dataType": "post",
  "communityName": "r/datascience",
  "datetime": "2024-05-07",
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}
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