6 Matching Annotations
  1. Jun 2019
    1. acf(as.vector(diff(diff(co2),lag=12))
      1. diff
      2. seasonal diff
    2. etete_{t} is independent of Yt−1,Yt−2,…Yt−1,Yt−2,…Y_{t-1},Y_{t-2},\dots. For this model, ρk=0ρk=0\rho_{k}=0 and ρk=Φρk−12ρk=Φρk−12\rho_{k}=\Phi\rho_{k-12} for k≥1

      ρk = autocorrelation of series at lag k

    3. |ϕ|<1|ϕ|<1|\phi|<1, which ensures stationarity

      |ϕ|<1 seasonal autoregressive parameter

  2. Jul 2018
    1. However, price time-series 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 time-series are usually non-stationary, that is their statistical properties are less stable over time.
  3. Jun 2018
  4. Sep 2017