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 Nov 2020
 Apr 2020

github.com github.com

To take full advantage of tabular augmentation for timeseries you would perform the techniques in the following order: (1) transforming, (2) interacting, (3) mapping, (4) extracting, and (5) synthesising
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 Jun 2019

tgrrr.github.io tgrrr.github.io

acf(as.vector(diff(diff(co2),lag=12))
 diff
 seasonal diff

etete_{t} is independent of Yt−1,Yt−2,…Yt−1,Yt−2,…Y_{t1},Y_{t2},\dots. For this model, ρk=0ρk=0\rho_{k}=0 and ρk=Φρk−12ρk=Φρk−12\rho_{k}=\Phi\rho_{k12} for k≥1
ρk = autocorrelation of series at lag k

ϕ<1ϕ<1\phi<1, which ensures stationarity
ϕ<1 seasonal autoregressive parameter

 Jul 2018

www.learndatasci.com www.learndatasci.com

However, price timeseries have some drawbacks. Prices are usually only positive, which makes it harder to use models and approaches which require or produce negative numbers. In addition, price timeseries are usually nonstationary, that is their statistical properties are less stable over time.

 Jun 2018

www.youtube.com www.youtube.com

Analyzing and Modeling Time Series Gene Expression with STEM and DREM
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 Sep 2017

brendanyounger.com brendanyounger.com