# HTM loss its prediction ability

**URL:** <https://discourse.numenta.org/t/htm-loss-its-prediction-ability/2611>\
**Category:** Engineering\
**Tags:** anomaly-detection, prediction, htm\
**Created:** [July 31, 2017, 11:26am UTC](https://discourse.numenta.org/t/htm-loss-its-prediction-ability/2611 "2017-07-31T11:26:15Z")\
**Posts on this page:** 1\
**Showing post:** 13

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**Author:** ![Pegasus](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/pegasus/32/1383_2.png) [@Pegasus](https://discourse.numenta.org/u/Pegasus)\
**Post date:** [August 2, 2017, 9:35am UTC](https://discourse.numenta.org/t/htm-loss-its-prediction-ability/2611/13 "2017-08-02T09:35:27Z")

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Hi, Jacob,  
Thanks, Yes, indeed, the time is meaningful to the data. We can easily find the periodic waveform from the image, In effect, one cycle is just a day, as the records generated every half an hour, so 48 records together make up one cycle.

Thanks for your patient and detailed response, thanks sincerely.  
I have learned much from your answers. But I am still in puzzle about the question below, if you will give me a hand, I will appreciate it greatly.

> [@Pegasus](#):
>
> The HTM as often the case fails to predict when anomaly occur, because the likelihoods of its predictions are ridiculous then abandoned, instead of the real real data at that time.
> 
> Do I get the point? I refer to the source code, there indeed are so called ‘best prediction’ but I couldn’t find the logic of mimicking raw data, and I wonder how the likelihoods of prediction( not the likelihood value of anomaly) are calculated ? Does the process of calculation take advantage of the real data in advance? Still, I have no idea where to get these information in the source code.

Thanks.

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