Greetings!
As part of my research project I am using R to study temperature data
collected by a network. Each node (observation point) records temperature of
its surroundings throughout the day and generates a dataset. Using the
recorded datasets for the past 7 days I need to build a prediction model for
each node that would enable it to check the observed data against the
predicted data. How can I derive an equation for temperature using the
datasets?
The following is a subset of one of the datasets:-
Time Temperature
07:00:17.369668 17.509
07:03:17.465725 17.509
07:04:17.597071 17.509
07:05:17.330544 17.509
07:10:47.838123 17.5482
07:14:16.680696 17.5874
07:16:46.67457 17.5972
07:29:16.887654 17.7442
07:29:46.705759 17.754
07:32:17.131713 17.7932
07:35:47.113953 17.8324
07:36:17.194981 17.8324
07:37:17.227013 17.852
07:38:17.809174 17.8618
07:38:48.00011 17.852
07:39:17.124362 17.8618
07:41:17.130624 17.8912
07:41:46.966421 17.901
07:43:47.524823 17.95
07:44:47.430977 17.95
07:45:16.813396 17.95
So far I have tried to use linear model fit but have not found it to be
useful. You may look at the attached graph for further reference.
http://www.nabble.com/file/p25995874/Regression%2BModel%2Bfor%2BNode%2B1%2BDay%2B1.png
Regression+Model+for+Node+1+Day+1.png
I would really appreciate if you could suggest the correct approach to
building such a prediction model.
Many thanks,
Aneeta
--
View this message in context:
http://www.nabble.com/Temperature-Prediction-Model-tp25995874p25995874.html
Sent from the R help mailing list archive at Nabble.com.
Hi, On Oct 21, 2009, at 12:31 PM, Aneeta wrote:> > Greetings! > > As part of my research project I am using R to study temperature data > collected by a network. Each node (observation point) records > temperature of > its surroundings throughout the day and generates a dataset. Using the > recorded datasets for the past 7 days I need to build a prediction > model for > each node that would enable it to check the observed data against the > predicted data. How can I derive an equation for temperature using the > datasets? > The following is a subset of one of the datasets:- > > Time Temperature > > 07:00:17.369668 17.509 > 07:03:17.465725 17.509 > 07:04:17.597071 17.509 > 07:05:17.330544 17.509 > 07:10:47.838123 17.5482 > 07:14:16.680696 17.5874 > 07:16:46.67457 17.5972 > 07:29:16.887654 17.7442 > 07:29:46.705759 17.754 > 07:32:17.131713 17.7932 > 07:35:47.113953 17.8324 > 07:36:17.194981 17.8324 > 07:37:17.227013 17.852 > 07:38:17.809174 17.8618 > 07:38:48.00011 17.852 > 07:39:17.124362 17.8618 > 07:41:17.130624 17.8912 > 07:41:46.966421 17.901 > 07:43:47.524823 17.95 > 07:44:47.430977 17.95 > 07:45:16.813396 17.95I think you/we need much more information. Are you really trying to build a model that predicts the temperature just given the time of day? Given that you're in NY, I'd say 12pm in August sure feels much different than 12pm in February, no? Or are you trying to predict what one sensor readout would be at a particular time given readings from other sensors at the same time? Or ... ? -steve -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact
Hi, In order to have more eyes on this, I'm CCing this back to lease (please try to keep further correspondence here, since most mail to *this* address of mine probably gets lost if it's not coming in from a list to begin with) ... I'm not really sure I have much to say about your problem ... it seems you're working in an open research area that I have 0 knowledge about. I'd recommend you talk with your advisor a bit to see what models they have in mind ... I'd imagine questions directed to this list will be better responded to when they're more specific, eg. some problem about using a particular library/model/package to perform a specific task, and not more open ended ones like which equation you can use to fit your data (or even if that's a reasonable thing to do, at all). Anyway, sorry .. don't have much valuable input, but good luck all the same. -steve On Oct 21, 2009, at 1:07 PM, Aneeta Bhattacharyya wrote:> Hi Steve, > > Thanks for your email. > The data that I have has been collected by a sensor network deployed > by Intel. You may take a look at the network at the following > website http://db.csail.mit.edu/labdata/labdata.html > > The main goal of my project is to simulate a physical layer attack > on a sensor network and to detect such an attack. In order to detect > an attack I need to have a model that would define the normal > behaviour. So the actual variation of temperature throughout the > year is not very important out here. I have a set of data for a > period of 7 days which is assumed to be the correct behaviour and I > need to build a model upon that data. I may refine the model later > on to take into account temperature variations throughout the year. > > Yes I am trying to build a model that will predict the temperature > just on the given time of the day so that I am able to compare it > with the observed temperature and determine if there is any > abnormality. Each node should have its own expectation model (i.e. > there will be no correlation between the readings of the different > nodes). > > Please let me know if you have any further questions. > > Many Thanks, > Aneeta > > > On Wed, Oct 21, 2009 at 12:46 PM, Steve Lianoglou <mailinglist.honeypot at gmail.com > > wrote: > Hi, > > On Oct 21, 2009, at 12:31 PM, Aneeta wrote: > > > Greetings! > > As part of my research project I am using R to study temperature data > collected by a network. Each node (observation point) records > temperature of > its surroundings throughout the day and generates a dataset. Using the > recorded datasets for the past 7 days I need to build a prediction > model for > each node that would enable it to check the observed data against the > predicted data. How can I derive an equation for temperature using the > datasets? > The following is a subset of one of the datasets:- > > Time Temperature > > 07:00:17.369668 17.509 > 07:03:17.465725 17.509 > 07:04:17.597071 17.509 > 07:05:17.330544 17.509 > 07:10:47.838123 17.5482 > 07:14:16.680696 17.5874 > 07:16:46.67457 17.5972 > 07:29:16.887654 17.7442 > 07:29:46.705759 17.754 > 07:32:17.131713 17.7932 > 07:35:47.113953 17.8324 > 07:36:17.194981 17.8324 > 07:37:17.227013 17.852 > 07:38:17.809174 17.8618 > 07:38:48.00011 17.852 > 07:39:17.124362 17.8618 > 07:41:17.130624 17.8912 > 07:41:46.966421 17.901 > 07:43:47.524823 17.95 > 07:44:47.430977 17.95 > 07:45:16.813396 17.95 > > I think you/we need much more information. > > Are you really trying to build a model that predicts the temperature > just given the time of day? > > Given that you're in NY, I'd say 12pm in August sure feels much > different than 12pm in February, no? > > Or are you trying to predict what one sensor readout would be at a > particular time given readings from other sensors at the same time? > > Or ... ? > > -steve > > -- > Steve Lianoglou > Graduate Student: Computational Systems Biology > | Memorial Sloan-Kettering Cancer Center > | Weill Medical College of Cornell University > Contact Info: http://cbio.mskcc.org/~lianos/contact > >-- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact
Check out the forecast package. On Wed, Oct 21, 2009 at 12:31 PM, Aneeta <anestays at cs.sunysb.edu> wrote:> > Greetings! > > As part of my research project I am using R to study temperature data > collected by a network. Each node (observation point) records temperature of > its surroundings throughout the day and generates a dataset. Using the > recorded datasets for the past 7 days I need to build a prediction model for > each node that would enable it to check the observed data against the > predicted data. How can I derive an equation for temperature using the > datasets? > The following is a subset of one of the datasets:- > > ? ? ?Time ? ? ? ? ? ? ?Temperature > > 07:00:17.369668 ? 17.509 > 07:03:17.465725 ? 17.509 > 07:04:17.597071 ? 17.509 > 07:05:17.330544 ? 17.509 > 07:10:47.838123 ? 17.5482 > 07:14:16.680696 ? 17.5874 > 07:16:46.67457 ? ? 17.5972 > 07:29:16.887654 ? 17.7442 > 07:29:46.705759 ? 17.754 > 07:32:17.131713 ? 17.7932 > 07:35:47.113953 ? 17.8324 > 07:36:17.194981 ? 17.8324 > 07:37:17.227013 ? 17.852 > 07:38:17.809174 ? 17.8618 > 07:38:48.00011 ? ? 17.852 > 07:39:17.124362 ? 17.8618 > 07:41:17.130624 ? 17.8912 > 07:41:46.966421 ? 17.901 > 07:43:47.524823 ? 17.95 > 07:44:47.430977 ? 17.95 > 07:45:16.813396 ? 17.95 > > So far I have tried to use linear model fit but have not found it to be > useful. You may look at the attached graph for further reference. > http://www.nabble.com/file/p25995874/Regression%2BModel%2Bfor%2BNode%2B1%2BDay%2B1.png > Regression+Model+for+Node+1+Day+1.png > I would really appreciate if you could suggest the correct approach to > building such a prediction model. > > Many thanks, > Aneeta > > > > > -- > View this message in context: http://www.nabble.com/Temperature-Prediction-Model-tp25995874p25995874.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >