1 / 7

Time series of the day

Time series of the day. Stat 153 - 11 Sept 2008 D. R. Brillinger Simple descriptive techniques. Trend X t =  + t +  t. Filtering y t =  r=-q s a r x t-r Simple moving average s = q , a r = 1/(2q+1) Filters may be in series. Differencing

afi
Download Presentation

Time series of the day

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Time series of the day

  2. Stat 153 - 11 Sept 2008 D. R. Brillinger Simple descriptive techniques Trend Xt =  + t + t Filtering yt = r=-qs ar xt-r Simple moving average s = q , ar = 1/(2q+1) Filters may be in series

  3. Differencing yt = xt - xt-1 =  xt "removes" linear trend Seasonal variation model Xt = mt + St + t St St-s 12 xt = xt - xt-12 , t in months

  4. Stationary case, autocorrelation estimate at lag k, rk t=1N-k (xt- )(xt+k - ) over t=1N (xt - )2 autocovariance estimate at lag k, ck t=1N-k (xt - )(xt+k - ) / N

  5. Departures from assumptions Nonstationarity Trend - OLS Seasonality - trig functions Outliers Missing values

More Related