  Detecting transient light curves (e.g., transiting planets) requires high
precision data, and thus it is important to effectively filter systematic
trends affecting ground based wide field surveys. We apply an implementation of
the Trend Filtering Algorithm (TFA) (Kovacs et al. 2005) to the 2MASS
calibration catalog and select Palomar Transient Factory (PTF) photometric time
series data. TFA is successful at reducing the overall dispersion of light
curves, however it may over filter intrinsic variables and increase
"instantaneous" dispersion when a template set is not judiciously chosen. In an
attempt to rectify these issues we modify the original literature TFA by
including measurement uncertainties in its computation, including ancillary
data correlated with noise, and algorithmically selecting a template set using
clustering algorithms as suggested by various authors. This approach may be
particularly useful for appropriately accounting for variable photometric
precision surveys and/or combined data-sets. In summary, our contributions are
to provide a MATLAB software implementation of TFA and a number of
modifications tested on synthetics and real data, summarize the performance of
TFA and various modifications on real ground based data sets (2MASS and PTF),
and assess the efficacy of TFA and modifications using synthetic light curve
tests consisting of transiting and sinusoidal variables. While the transiting
variables test indicates that these modifications confer no advantage to
transit detection, the sinusoidal variables test indicates potential
improvements in detection accuracy.
