Here is a discussion (with links to an earlier discussion) on seeing... and even «detecting»... step changes in
synthetic data sets that we know for a fact have simply an underlying linear trend plus noise: (snip.
A second approach would use
a synthetic data set that substitutes values for the original data set and then tests to see whether the synthetic data yield results that «are good enough» to satisfy the researcher.
Not exact matches
The consortium will oversee the development and delivery of the cameras, and take the lead in supporting the UK solar physics community in their use of DKIST by providing a
set of processing tools for DKIST
data,
synthetic observations to validate diagnostic approaches, and support for developing observing proposals.
To project that trend forward, the team then used models recently developed to analyze Antarctic ice sheet collapse, plus large global
data sets to tailor specific Atlantic tropical cyclone
data and create «
synthetic» storms to simulate future weather patterns.
Second, to generate
synthetic data, we must train on large
data sets of real - world
data and have balanced and diverse
data sets.