I have a time series data
c1= c(0.558642328,
0.567173803,
0.572518969,
0.579917556,
0.592155421,
0.600239837,
0.598955071,
0.608857572,
0.615442061,
0.613502347,
0.618076897,
0.626769781,
0.633930194,
0.645518577,
0.66773088,
0.68128165,
0.695552504,
0.6992836,
0.702771866,
0.700840271,
0.684032428,
0.665082645,
0.646948862,
0.621813893,
0.597888613,
0.577744126,
0.555984044,
0.533597678,
0.523645413,
0.522041142,
0.525437844,
0.53053292,
0.543152606,
0.549038792,
0.555300856,
0.563411331,
0.572663951,
0.584438777,
0.589476192,
0.604197562,
0.61670388,
0.624161184,
0.624345171,
0.629342985,
0.630379665,
0.620067096,
0.597480375,
0.576228619,
0.561285031,
0.543921304,
0.530826211,
0.519563568,
0.514228535,
0.515202665,
0.516663855,
0.525673366,
0.543545395,
0.551681638,
0.558951402,
0.566816133,
0.573842585,
0.578611696,
0.589180577,
0.603297615,
0.624550509,
0.641310155,
0.655093217,
0.668385196,
0.671600127,
0.658876967,
0.641041982,
0.605081463,
0.585503519,
0.556173635,
0.527428073,
0.502755737,
0.482510734,
0.453295642,
0.439938772,
0.428757811,
0.422361642,
0.40945864,
0.399504355,
0.412688798,
0.42684828,
0.456935656,
0.48355422,
0.513727218,
0.541630101,
0.559122121,
0.561763656,
0.572532833,
0.576761365,
0.576146233,
0.580199403,
0.584954906)
corresponding to dates
dates = seq(as.Date("2016-09-01"), as.Date("2020-07-30"), by=15)
What I want to do is compute normalized spectral entropy of this time series. I have found in literature that high value indicates high stability of a system.
I have found a function here: https://rdrr.io/cran/ForeCA/man/spectral_entropy.html, but cannot generate what I want. New to this topic, and hence any interpretation would be helpful too.
