# machine learning on data with lots of fluctuation

I have CSV files that contains data of Cache performance on a source with different workloads for a particular time period ! For each time interval data is recorded , It includes columns like ReadHits , WriteHits , Cacheusage , ReadMiss Etc .

   Ex of CSV FILE contents:

1       ,  150  , 0   ,  15474 , 12

2       ,   0    , 0   , 700375, 245

3       ,  15426 ,  1546 , 45121,195


Note : Each interval will be of same time period , Eg 1 interval = 40Sec

In each column data will be from 0 to 60k+ , this varies for each interval !!

   Eg : Interval 7    8    9    10  11

Readhits 0   240  1680   0  2091


So this way it contains data with lots of fluctuation ranging between 0 and 60k+

Suppose i have data till 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 close to actual values !

Which algorithm will be better on this kind of data for predicting data of next intervals?

Apart from prediction what other useful and innovative things i can do from Machine learning algorithms for above kind of data that can be useful for the user ?

• Did you try log transform? That is really focusing on the size of values, and often indicated when values is wildly different. Tell us if that helped! – kjetil b halvorsen Apr 14 '17 at 11:50