> On Dec 1, 2016, at 1:58 PM, Ashwini Patil wrote:
>
> Hi David,
>
> here is my code including what i did for the tsboot:
> rm(list = ls())
> library(boot)
> library(tseries)
> library(TTR)
> library(quantmod)
> library(scales)
> library(forecast)
> library(zoo)
> library(TSA)
> security<-"NFLX
Hi David,
here is my code including what i did for the tsboot:
rm(list = ls())
library(boot)
library(tseries)
library(TTR)
library(quantmod)
library(scales)
library(forecast)
library(zoo)
library(TSA)
security<-"NFLX"
startDate<-"2012-06-01"
endDate<-"2016-10-31"
qte_list<-c("AdjClose")
data=get.
Just briefly to follow up David's comment, though this is mainly about
statistics and therefore off topic here...
Bootstrapping time series is a subtle issue that requires familiarity
with the technical details-- and maybe even current research. The
tsboot() function gives you several options from
> On Dec 1, 2016, at 7:45 AM, Ashwini Patil wrote:
>
> Hi,
>
> I want to implement a bootstrap method for time series.
> I am taking the adj close values from yahoo for NFLX and now I need to
> bootstrap these values using ARIMA model.
>
> here is my code so far:
> rm(list = ls())
> library(bo
Hi,
I want to implement a bootstrap method for time series.
I am taking the adj close values from yahoo for NFLX and now I need to
bootstrap these values using ARIMA model.
here is my code so far:
rm(list = ls())
library(boot)
library(tseries)
library(TTR)
library(quantmod)
library(scales)
librar
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