Hi Advait,
if you want to keep all time properties, the package "timetools" may
help you to do so.
This package defines the class "TimeIntervalDataFrame" which can be a
good start to look at.
Best,
Vlad
-----
Vladislav Navel
SCAN
www.scan-datamining.com
Le 13/03/2012 11:03, Advait Godbole a écrit :
I want to run the analysis on hourly data, in the sense that I would like
to retain the hourly resolution while still being able to identify seasonal
variation and month-to-month (or week-to-week) trends. I am not sure
whether I am being clear in a mathematical sense, or if the above is
possible - please excuse, I am new to time series.
Thanks!
advait
On Tue, Mar 13, 2012 at 3:22 PM, Hodgess, Erin<hodge...@uhd.edu> wrote:
**
Do you want to run the analysis on the hourly data or some aggregate of
it, please?
thanks,
erin
Erin M. Hodgess, PhD
Associate Professor
Department of Computer and Mathematical Sciences
University of Houston - Downtown
mailto: hodge...@uhd.edu
-----Original Message-----
From: r-sig-geo-boun...@r-project.org on behalf of Advait Godbole
Sent: Tue 3/13/2012 4:41 AM
To: r-sig-geo@r-project.org
Subject: [R-sig-Geo] hourly time series
Dear all,
I have one year's worth of hourly data, starting from 1st April 2010 and
ending on 31st March 2011. I would like to perform time series analysis on
it. Not having used "ts" before, I am having trouble setting it up to
correctly represent the data. I have R reading in the time series via:
*wind_ts<- ts(wind.MH,start=1,frequency=1)*
where "wind.MH" is a 8760x1 matrix object. I then tried to decompose the
time series with the following error:
*wind_ts_components<- decompose(wind_ts)*
*Error in decompose(wind_ts) : time series has no or less than 2 periods*
The dataset is the hourly wind generation for Maharashtra, India and has
some seasonality associated with the Indian monsoon. Ultimately, this is
what I would like to identify. I imagine that correctly setting the "start"
and "frequency" parameters is necessary to be able to parse the dataset
into months and seasons.
I would greatly appreciate help on how to handle this and any leads on time
series analysis for hourly data in general.
Regards,
--
advait godbole
analyst, prayas energy group
pune, india
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