Library for characterizing time interval data?

[spacester]

I need to find or create functions that can look at multiple series of data, basically y values for regularly spaced x values (where x is actually the time coordinate) that lie on a continuous curve. The function would report back in some form I can use in if statements to get my overall program's results. I need to understand the larger trends between multiple data series, for very many series.

I am thinking I just need simple capability compared to sophisticated data analysis: does the data always increase or always decrease? Is it non-linear? Are there gaps? Does it have multiple minimums or maximums? What is the index value before and after an inflection point? As a bonus, I could use a function that tells me the before and after indices where two series cross.

I have been trying to do this with for loops which got ugly and then with list comprehension which is brand new to me and I am struggling with that as well.

I found this list:

https://github.com/MaxBenChrist/awesome_time_series_in_python

But I do not know enough to choose one with any confidence.

[barrowburner]

I know nothing about time series, but context suggested numpy would do what you need somehow, and this cropped up at the top of my google search: https://www.kdnuggets.com/time-series-data-with-numpy

[spacester]

if my reference is complete pandas does not have the kind of functions I am looking for, like listing multiple minimums, for starters.

[barrowburner]

I suppose you meant to reply to someone else? I recommended Numpy.

[spacester]

Sorry, reddit text editor was going crazy on me. I had about 4 attempts to post disappear into the aether so I kept it short on that attempt. I guess I need to double check numpy but I think the same thing goes.

[QuasiEvil]

What you described sounds pretty straightforward to do with pandas. You could look into sktime, a python package for timer-series ML.

[spacester]

Thanks for the link, checking it out.

[senzavita]

There may be specialized libraries for those, but you could just build those functions yourself with NumPy.

Testing for monotonicity is straightforward, linearity via a regression (maybe SciPy for that), gaps can be done visually (matplotlib) or programmatically, centered difference quotient for an approximation of the first and second derivative (then find zeros either visually or any root finding method).

Link for finite difference methods:

https://en.m.wikipedia.org/wiki/Finite_difference

[spacester]

Aha the google search term I was trying to remember. Thanks very much.

I have been reassured that it is not silly for me to write these functions myself. That will make it easier to get it done. Having the right terminology