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@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.