We have the following data points in variable
data pertaining to a problem that we are solving:
9996792524 8479115468 11394750532 9594869828 10850291677 10475635302 10116010939 11206949341 11975140317 11526960332 9986194500 11501088256 11833183163 13246940910 13255698568 13775653990 13567323648 14607415705 13835444224 14118970743
This corresponding date numbers are stored in a variable
735678.574305556 735710.586805556 735863.672916667 735888.539583333 735921.589583333 735941.590972222 735986.583333333 736021.481944444 736043.498611111 736063.5 736083.504166667 736223.35625 736250.45 736278.452083333 736314.327777778 736356.239583333 736383.209722222 736411.10625 736431.925694444
We fit a 9th degree polynomial to this data and then plot it as follows:
data9 = fit( timevalues, data, 'poly9', 'Normalize', 'on' ); plot(data9,timevalues,data);
Now we need to extrapolate this trend / polynomial into the future or for further values of
timevalues on the X-axis. How do we do that?
UPDATE: Description of our problem
We have bits per second observed on our border firewall device -- which is what these values are. There are a LOT of such values over 1 minute intervals in the last 4 years (more than a million). Not all values are useful because we just want to see how the trends in peaks is rising in time since we want to increase our load capacity before we hit 'max' some day. In other words, we are not interested in valleys or average values but 'peaks'. So we used the
findpeaks() function in Matlab to find the peaks in our data (which is what the values above are). Now we are trying to fit a trend line on these peaks and extrapolate it to see how we need to increase capacity on border device.