# Machine learning on data with lots of fluctuation

I have CSV files that contain data of cache performance of a source with different workloads for a particular time period. For each time interval, data is recorded, includeing columns like ReadHits, WriteHits, Cacheusage, ReadMiss, etc.

Example contents:

   Interval,ReadHits,WriteHits,Cacheusage,ReadMiss

1       ,  150  , 0   ,  15474 , 12

2       ,   0    , 0   , 700375, 245

3       ,  15426 ,  1546 , 45121,195


Note: Each interval will be of same time period, e.g. 1 interval = 40 seconds.

In each column, data will be from 0 to 60k+; this fluctuates for each interval. Example:

     interval 7    8    9    10  11

Readhits 0   240  1680   0  2001


Suppose I have data until 60 intervals. How can I predict data from intervals 61 to 70?

I have used ARIMA model, random forest, kmeans and different machine learning algorithms but have never been able to predict anything close to actual values!

Which algorithm will be better on this kind of data for predicting data of the next intervals? Is there anything useful that can be extracted from this information?

## migrated from ai.stackexchange.comApr 18 '17 at 19:20

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