Derivative of experimental data

*To*: mathgroup at smc.vnet.net*Subject*: [mg124733] Derivative of experimental data*From*: Robert McHugh <bob_mchugh_2000 at yahoo.com>*Date*: Fri, 3 Feb 2012 02:09:10 -0500 (EST)*Delivered-to*: l-mathgroup@mail-archive0.wolfram.com*Reply-to*: Robert McHugh <bob_mchugh_2000 at yahoo.com>

Gabriel, I have had good results for a similar problem using the methods described in the link below. http://www.holoborodko.com/pavel/numerical-methods/numerical-derivative/smooth-low-noise-differentiators/ The algorithm described above can be implemented very efficiently with ListConvolve For the system I analyzed, the system has a 24 hour period and measurements are recorded every minute. While the general trend of the data was clear to a human observer, the data was noisy and the analysis required estimates of the derivative of the data. In this case I used N = 51 (i.e. 25 data points on either side of the time of interest). Best Regards, Bob