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#TimeSeries

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PaquitoBernard

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.

#academia#research#climatechange
Public
VictoriaMetrics

🚀 VictoriaMetrics Playgrounds!
Did you know that #VictoriaMetrics offers interactive Playgrounds to enhance your experience?

🔍 #Test the #query engine: Experiment with queries to uncover valuable insights.
🔧 Relabeling playground: Refine and transform your metrics effortlessly.
Whether testing, learning, or optimizing VictoriaMetrics Playgrounds supports your journey to better data insights.

🎯 Ready to explore? Try today at play.victoriametrics.com
#TimeSeries #Observability #OpenSource

Public
Joris Meys

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

Cutout of figure 1 from the paper. It shows the day of the year spring phenology is detected for the pedunculate oak in southern UK and the cherry tree in Japan. For the oak the day when the leaves come out is tracked through time, and for the cherry tree it's the day of blossoming. For the oak you see a dataset from the 18th century that shows on average day 110 as day all leaves are out. A second line starts at 1840 and goed until mid 20th century. Spring phenology starts between 100 and 110 days in that period. The third dataset shows this day dropping from about day 120 in 1950 to below 100 after 200. The cherry tree dataset starts in 1500 and is continuous. It stays largely stable around day 105. Around 1940 it starts to drop fast to reach day 90 after the year 2000.
#Science#climate#DataScience
Public
Thomas Arildsen

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?

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Pierre-Simon Laplace

🔴 𝐇𝐨𝐰 𝐓𝐨 𝐅𝐨𝐜𝐮𝐬 𝐎𝐧 𝐖𝐡𝐚𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐈𝐧 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠
🔗 learnbayesstats.com/episode/12

✅ 𝐈𝐧 𝐭𝐡𝐢𝐬 𝐄𝐩𝐢𝐬𝐨𝐝𝐞, 𝐲𝐨𝐮’𝐥𝐥 𝐥𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐭𝐨 𝐚𝐜𝐡𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 with @pymc

Alex Andorra & Jesse Grabowski talk about state space models, simplifying forecasting, applications etc.

#LearningBayesianStatistics#PyMC#forecasting
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