[R] which model suits for these kind of data

[Uwe Ligges]

You are probably under Windows. The Menu to install from local zip files 
implies you specify a precompiled binary package.


Uwe Ligges



On 24.06.2010 06:12, vijaysheegi wrote:
>
> Hi ,
> i am trying to install tseries packages from local drive.i am getting
> .Please advise
>
> I am getting this errror
> Please advise
>
>> utils:::menuInstallLocal()
> Error in gzfile(file, "r") : cannot open the connection
> In addition: Warning message:
> In gzfile(file, "r") :
>    cannot open compressed file 'timeSeries/DESCRIPTION', probable reason 'No
> such file or directory'
>>
>
>
> On 6/22/10, Gabor Grothendieck [via R]<
> ml-node+2263915-1199101214-288333 at n4.nabble.com<ml-node%2B2263915-1199101214-288333 at n4.nabble.com>>
> wrote:
>>
>> Try this:
>>
>> library(forecast)
>> f<- forecast(DF[,3]); f
>> plot(f)
>>
>>
>> On Tue, Jun 22, 2010 at 4:14 AM, vijaysheegi<[hidden email]<http://user/SendEmail.jtp?type=node&node=2263915&i=0>>
>> wrote:
>>
>>>
>>> Hi ,
>>> please  help me which model is helpful for  forecasting giving following
>>> inputs (inputs are not linear)
>>>
>>> sales date        shopnuber    total      20%profit  10%profit
>>> 2009-10-03      1       41891   2863    39028
>>> 2009-10-04      1       49152   7588    41564
>>> 2009-10-05      1       45804   23543   22261
>>> 2009-10-06      1       48395   48371   24
>>> 2009-10-07      1       48906   20204   28702
>>> 2009-10-08      1       47003   19442   27561
>>> 2009-10-09      1       46296   21635   24661
>>> 2009-10-10      1       45980   34791   11189
>>> 2009-10-11      1       48423   1483    46940
>>> 2009-10-12      1       48800   18500   30300
>>> 2009-10-13      1       40694   22068   18626
>>> 2009-10-14      1       47356   42361   4995
>>> 2009-10-15      1       41501   15964   25537
>>> 2009-10-16      1       44762   42296   2466
>>> 2009-10-17      1       48607   12023   36584
>>> 2009-10-18      1       47513   28275   19238
>>> 2009-10-19      1       44527   7927    36600
>>> 2009-10-20      1       43948   17981   25967
>>> 2009-10-21      1       43139   17682   25457
>>> 2009-10-22      1       42426   24454   17972
>>> 2009-10-23      1       43620   1689    41931
>>> 2009-10-24      1       40850   28595   12255
>>> 2009-10-25      1       41665   10339   31326
>>> 2009-10-26      1       46151   41662   4489
>>> 2009-10-27      1       41355   30207   11148
>>>
>>>
>>>
>>> With this data how to predict total sales for further 6 days.Someone
>> please
>>> help .(if we get totals sales we can obntain 20% and 10% profit)
>>>
>>> --
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>>>
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