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#TimeSeries

3 posts3 participants0 posts today

I had the pleasure of presenting this week at the @rladiesrome meetup about forecasting with linear regression. This workshop covered the following topics:
🔹Time series decomposition
🔹Correlation and seasonal analysis
🔹Modeling trend and seasonality
🔹Using piecewise regression to model change in trend
🔹Residuals analysis

The workshop recording is available online:
youtube.com/watch?v=lk3a3GQ7kc

#programming #graphing #plotting #visualization #timeSeries #gnuplot #commonLisp #lisp #example screwlisp.small-web.org/progra
I could not even find my own previous articles and #demos of this online!

I used #uiop run-program to handle one specific case like

(gnuplot "bad title" '((1 2) (3 4)) '((5 6) (7 8)))
or equivalently,
(apply 'gnuplot "bad title" '(((1 2) (3 4)) ((5 6) (7 8))))

Do you personally have an example? I remember it being hard to dredge up gnuplot examples but this is beyond silly.

I've been working with Time Series models a fair bit lately, and have noticed that ARIMA models (in particular) tend to regress to the long-term mean. This is usually a good thing, agreeing with the intuition that the least wrong guess about the future behaviour (of a stable system) is somewhere in that location (ie weather averages out to historical climate in the long run - in the absence of climate change, that is).

But it's made me wonder if these models might be the cause of consistent over|under-estimation of the future behaviour of systems undergoing fundamental changes over time?

eg Consistent underestimation of solar PV growth and overestimation of RBA wage-price figures.

I am looking for a postdoc 'Environmental and behavioral health in a changing climate' (1/2)

2 years in #Rennes #france #rstats #timeseries

We are looking for a postdoctoral researcher to help us understand the short-term impacts of environmental conditions on mental health, sleep and physical activity related behaviors. Future findings will help us better anticipate present and future consequences of climate change on bike use and sleep.

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#TimeSeries #Observability #OpenSource

Very interesting paper in #Nature showing the phenological shift of #plants due to #ClimateChange . They combined a number of datasets on the occurence of changes that indicate spring, like flowering or leaf out. I show two of the longest time series, the leaf-out dates in the UK recorded by the Marsham family, and the blooming of cherry trees in Japan, documented in diaries and chronicles.

nature.com/articles/s41558-022

I have a #timeseries of values at low temporal resolution where the values represent an average of the respective surrounding intervals.
I wish to up-sample this sequence to a higher temporal resolution in such a way that the average of the up-sampled values is equal to the corresponding value from the original time series.
Does an #algorithm for the kind of interpolation I am looking for exist? (not Pandas' resample or SciPy's signal.resample.) And is there an implementation of it in #Python?