Qualitative methods for the validation of dynamic models. It contains (i) an orthogonal set of deviance measures for absolute, relative and ordinal scale and (ii) approaches accounting for time shifts. The first approach transforms time to take time delays and speed differences into account. The second divides the time series into interval units according to their main features and finds the longest common subsequence (LCS) using a dynamic programming algorithm.
|Author||K. Gerald van den Boogaart [aut, ths], Stefanie Rost [aut], Thomas Petzoldt [aut, ths, cre]|
|Date of publication||2015-09-05 22:22:15|
|Maintainer||Thomas Petzoldt <email@example.com>|
|License||GPL (>= 2)|
compareME: Compute Several Deviance Measures for Comparison
EF: Efficiency Factor as Suggested by Nash & Sutcliffe
features: Qualitative Features of Time Series
GRI: A Geometric Reliability Index as Suggested by Leggett &...
LCS: Algorithm for the Longest Common Subsequence Problem
phyto: Observed and Predicted Data of Phytoplankton
qualV: Qualitative Validation Methods
quantV: Quantitative Validation Methods
qvalLCS: Qualitative Validation by Means of Interval Sequences and LCS
timetrans: Bijective Transformations of Time
timeTransME: Transformation of Time to Match Two Time Series