How to output "Trace" list from auto.arima in forecast library
Works like magic!! Thanks Jeff and Joshua.
There is a clear advantage in running lower order ARIMA models with drift in
comparison to what auto.arima gives. The AIC values just differ by a few
points so there isn't really much to choose based just on AIC. The competing
model however offers significant reduction in prediction error.
Thanks for the help again.
-----Original Message-----
From: Joshua Ulrich [mailto:josh.m.ulrich at gmail.com]
Sent: 03 October 2011 19:58
To: Jeffrey Ryan
Cc: Arun Krishnamoorthy; r-sig-finance at r-project.org
Subject: Re: [R-SIG-Finance] How to output "Trace" list from auto.arima in
forecast library
In addition to sink(), you can also use capture.output(). Then you
can read the contents of "out" into a new object.
out <- capture.output({
Fit <- auto.arima(data_ts, trace=TRUE)
})
outData <- read.table(con <- textConnection(out), sep=":")
close(con)
Best,
--
Joshua Ulrich ?| ?FOSS Trading: www.fosstrading.com
On Mon, Oct 3, 2011 at 7:36 AM, Jeffrey Ryan <jeffrey.ryan at lemnica.com>
wrote:
Take a look at ?sink The trace is just a series of calls to 'cat', so it won't be particularly useful unless you parse. Jeff On Mon, Oct 3, 2011 at 2:18 AM, Arun Krishnamoorthy <arun.k at bridgei2i.com> wrote:
Hi, I'm working on the forecast library and am using the auto.arima function with some dummy data Fit<-auto.arima(data_ts, trace=TRUE) I understand that trace evaluates alternative models and provides the corresponding AIC/SIC values with an output that looks like this; ARIMA(2,1,2) with drift ? ? ? ? : 1278.988 ?ARIMA(0,1,0) with drift ? ? ? ? : 1281.161 ?ARIMA(1,1,0) with drift ? ? ? ? : 1280.325 ?ARIMA(0,1,1) with drift ? ? ? ? : 1278.2 ?ARIMA(1,1,1) with drift ? ? ? ? : 1280.229 ?ARIMA(0,1,2) with drift ? ? ? ? : 1278.824 ?ARIMA(1,1,2) with drift ? ? ? ? : 1281.911 ?ARIMA(0,1,1) ? ? ? ? ? ? ? ? ? ?: 1277.73 ?ARIMA(1,1,1) ? ? ? ? ? ? ? ? ? ?: 1279.804 ?ARIMA(0,1,0) ? ? ? ? ? ? ? ? ? ?: 1281.265 ?ARIMA(0,1,2) ? ? ? ? ? ? ? ? ? ?: 1278.626 ?ARIMA(1,1,2) ? ? ? ? ? ? ? ? ? ?: 1281.64 ?Best model: ARIMA(0,1,1) Unfortunately, Trace is not an object and i'm unable to read it in to a
data
frame to do further diagnosis. Can someone please help me with how i can get this output into a data
frame.
I'm unable to do data.frame since i can't coerce an ARIMA object into
this
class. Sorry if this has been addressed before. I just think there may be some
data
series where running a closely competing alternative model may be useful Thanks, AK -----Original Message----- From: r-sig-finance-bounces at r-project.org [mailto:r-sig-finance-bounces at r-project.org] On Behalf Of Daniel Cegielka Sent: 26 September 2011 17:20 To: chrisbird Cc: r-sig-finance at r-project.org Subject: Re: [R-SIG-Finance] Filtering dates/times from zoo/xts series 2011/9/26 chrisbird <chris at chrisbird.com>
Thanks Brian, I did try using the ['T09:00/T21:00'] method for extraction but it did
not
return anything - I will reinvestigate this and see if I can get it working.
It works... you are sure that the data was ok? Very strange that you have received nothing...
d<-xts(1:25, Sys.time() + 1:25)> d ? ? ? ? ? ? ? ? ? ?[,1]
2011-09-26 13:26:55 ? ?1 2011-09-26 13:26:56 ? ?2 2011-09-26 13:26:57 ? ?3 2011-09-26 13:26:58 ? ?4 2011-09-26 13:26:59 ? ?5 2011-09-26 13:27:00 ? ?6 2011-09-26 13:27:01 ? ?7 2011-09-26 13:27:02 ? ?8 2011-09-26 13:27:03 ? ?9 2011-09-26 13:27:04 ? 10 2011-09-26 13:27:05 ? 11 2011-09-26 13:27:06 ? 12 2011-09-26 13:27:07 ? 13 2011-09-26 13:27:08 ? 14 2011-09-26 13:27:09 ? 15 2011-09-26 13:27:10 ? 16 2011-09-26 13:27:11 ? 17 2011-09-26 13:27:12 ? 18 2011-09-26 13:27:13 ? 19 2011-09-26 13:27:14 ? 20 2011-09-26 13:27:15 ? 21 2011-09-26 13:27:16 ? 22 2011-09-26 13:27:17 ? 23 2011-09-26 13:27:18 ? 24 2011-09-26 13:27:19 ? 25> d["2011-09-26 13:27:00/2011-09-26 13:27:10"] ? ? ? ? ? ? ? ? ? [,1] 2011-09-26 13:27:00 ? ?6 2011-09-26 13:27:01 ? ?7 2011-09-26 13:27:02 ? ?8 2011-09-26 13:27:03 ? ?9 2011-09-26 13:27:04 ? 10 2011-09-26 13:27:05 ? 11 2011-09-26 13:27:06 ? 12 2011-09-26 13:27:07 ? 13 2011-09-26 13:27:08 ? 14 2011-09-26 13:27:09 ? 15 2011-09-26 13:27:10 ? 16> d["T13:27:00/T13:27:10"] ? ? ? ? ? ? ? ? ?
?[,1]
2011-09-26 13:27:00 ? ?6 2011-09-26 13:27:01 ? ?7 2011-09-26 13:27:02 ? ?8 2011-09-26 13:27:03 ? ?9 2011-09-26 13:27:04 ? 10 2011-09-26 13:27:05 ? 11 2011-09-26 13:27:06 ? 12 2011-09-26 13:27:07 ? 13 2011-09-26 13:27:08 ? 14 2011-09-26 13:27:09 ? 15 2011-09-26 13:27:10 ? 16
The processing is not to remove non-trading days/holidays - I do that elsewhere. I'm doing processing on some complex strategies which use
some
instruments which trade a lot, but not everyday. I only wish to process
the
data from liquid days and strip out the less liquid data.
It's quite a sophisticated approach to data. Probably when you filter
using
the time you would have to count the number of observations and does not bind data below a certain level. I have no idea how to do it in an
elegant
way. best regards, daniel
I will certainly investigate quantstrat. Thanks, Chris. -- View this message in context:
http://r.789695.n4.nabble.com/Filtering-dates-times-from-zoo-xts-series-tp38
42937p3843408.html
Sent from the Rmetrics mailing list archive at Nabble.com.
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-- Jeffrey Ryan jeffrey.ryan at lemnica.com www.lemnica.com www.esotericR.com
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