  Certain instrumental effects and data reduction anomalies introduce
systematic errors in photometric time-series. Detrending algorithms such as the
Trend Filtering Algorithm (TFA) (Kov\'{a}cs et al. 2004) have played a key role
in minimizing the effects caused by these systematics. Here we present the
results obtained after applying the TFA, Savitszky-Golay (Savitzky & Golay
1964) detrending algorithms and the Box Least Square phase folding algorithm
(Kov\'{a}cs et al. 2002) to the TFRM-PSES data (Fors et al. 2013). Tests
performed on this data show that by applying these two filtering methods
together, the photometric RMS is on average improved by a factor of 3-4, with
better efficiency towards brighter magnitudes, while applying TFA alone yields
an improvement of a factor 1-2. As a result of this improvement, we are able to
detect and analyze a large number of stars per TFRM-PSES field which present
some kind of variability. Also, after porting these algorithms to Python and
parallelizing them, we have improved, even for large data samples, the
computing performance of the overall detrending+BLS algorithm by a factor of
$\sim$10 with respect to Kov\'{a}cs et al. (2004).
