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Sean Easter
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1.3578511 0.5119648 1.3189847 0.9214787 1.2272616 4.9167998 1.2272616 1.2272616 0.8854192 2.3386331 1.6132899 0.2030302 0.8426226 1.2277843 NA 1.3189847 1.3578511 0.8530141 2.3386331 1.0541099 0.7747481 0.5764672 1.3189847 1.2160533 1.2272616 0.6715839 0.9651803 1.6132899 1.2006974 0.6875047 1.3245534 1.2006974 0.8221709 1.3101684 1.6132899 1.6132899 1.2006974 1.3189847 1.0018480 1.2277843 1.4424190 1.6132899 1.2277843 1.2006974 0.7779642 0.9381081 0.8854192 NA NA 1.3189847 1.1070461 0.8221709 4.9167998 0.9214787 1.3189847 1.3189847 1.2277843 1.4424190 1.6132899 1.6132899 4.9167998 0.8235792 0.9708839 1.1070461 1.2160533 0.8354292 1.4424190 1.1958634 0.5119648 1.4424190 1.4424190 1.6132899 1.6132899 0.6710844 1.2272616 0.9708839 0.8890464 1.4424190 0.8890464 0.8221709 1.1958634 0.8132233 0.4630722 4.9167998 0.8890464 1.3189847 0.7373181 1.1070461 1.2279813 0.8890464 0.3588158 1.4424190 0.8132233 0.4297043 1.3578511 4.9167998 1.2272616 0.8426226 1.4424190 1.6132899 NA 

1.3578511
0.5119648
1.3189847
0.9214787
1.2272616
4.9167998
1.2272616
1.2272616
0.8854192
2.3386331
1.6132899
0.2030302
0.8426226
1.2277843
NA
1.3189847
1.3578511
0.8530141
2.3386331
1.0541099
0.7747481
0.5764672
1.3189847
1.2160533
1.2272616
0.6715839
0.9651803
1.6132899
1.2006974
0.6875047
1.3245534
1.2006974
0.8221709
1.3101684
1.6132899
1.6132899
1.2006974
1.3189847
1.0018480
1.2277843
1.4424190
1.6132899
1.2277843
1.2006974
0.7779642
0.9381081
0.8854192
NA
NA
1.3189847
1.1070461
0.8221709
4.9167998
0.9214787
1.3189847
1.3189847
1.2277843
1.4424190
1.6132899
1.6132899
4.9167998
0.8235792
0.9708839
1.1070461
1.2160533
0.8354292
1.4424190
1.1958634
0.5119648
1.4424190
1.4424190
1.6132899
1.6132899
0.6710844
1.2272616
0.9708839
0.8890464
1.4424190
0.8890464
0.8221709
1.1958634
0.8132233
0.4630722
4.9167998
0.8890464
1.3189847
0.7373181
1.1070461
1.2279813
0.8890464
0.3588158
1.4424190
0.8132233
0.4297043
1.3578511
4.9167998
1.2272616
0.8426226
1.4424190
1.6132899
NA

1.3578511 0.5119648 1.3189847 0.9214787 1.2272616 4.9167998 1.2272616 1.2272616 0.8854192 2.3386331 1.6132899 0.2030302 0.8426226 1.2277843 NA 1.3189847 1.3578511 0.8530141 2.3386331 1.0541099 0.7747481 0.5764672 1.3189847 1.2160533 1.2272616 0.6715839 0.9651803 1.6132899 1.2006974 0.6875047 1.3245534 1.2006974 0.8221709 1.3101684 1.6132899 1.6132899 1.2006974 1.3189847 1.0018480 1.2277843 1.4424190 1.6132899 1.2277843 1.2006974 0.7779642 0.9381081 0.8854192 NA NA 1.3189847 1.1070461 0.8221709 4.9167998 0.9214787 1.3189847 1.3189847 1.2277843 1.4424190 1.6132899 1.6132899 4.9167998 0.8235792 0.9708839 1.1070461 1.2160533 0.8354292 1.4424190 1.1958634 0.5119648 1.4424190 1.4424190 1.6132899 1.6132899 0.6710844 1.2272616 0.9708839 0.8890464 1.4424190 0.8890464 0.8221709 1.1958634 0.8132233 0.4630722 4.9167998 0.8890464 1.3189847 0.7373181 1.1070461 1.2279813 0.8890464 0.3588158 1.4424190 0.8132233 0.4297043 1.3578511 4.9167998 1.2272616 0.8426226 1.4424190 1.6132899 NA 

1.3578511
0.5119648
1.3189847
0.9214787
1.2272616
4.9167998
1.2272616
1.2272616
0.8854192
2.3386331
1.6132899
0.2030302
0.8426226
1.2277843
NA
1.3189847
1.3578511
0.8530141
2.3386331
1.0541099
0.7747481
0.5764672
1.3189847
1.2160533
1.2272616
0.6715839
0.9651803
1.6132899
1.2006974
0.6875047
1.3245534
1.2006974
0.8221709
1.3101684
1.6132899
1.6132899
1.2006974
1.3189847
1.0018480
1.2277843
1.4424190
1.6132899
1.2277843
1.2006974
0.7779642
0.9381081
0.8854192
NA
NA
1.3189847
1.1070461
0.8221709
4.9167998
0.9214787
1.3189847
1.3189847
1.2277843
1.4424190
1.6132899
1.6132899
4.9167998
0.8235792
0.9708839
1.1070461
1.2160533
0.8354292
1.4424190
1.1958634
0.5119648
1.4424190
1.4424190
1.6132899
1.6132899
0.6710844
1.2272616
0.9708839
0.8890464
1.4424190
0.8890464
0.8221709
1.1958634
0.8132233
0.4630722
4.9167998
0.8890464
1.3189847
0.7373181
1.1070461
1.2279813
0.8890464
0.3588158
1.4424190
0.8132233
0.4297043
1.3578511
4.9167998
1.2272616
0.8426226
1.4424190
1.6132899
NA
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chl
chl
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how How to predict missing values in time series?

iI have the following time series as

in which NANA are missing values,i and I want to predict/forecast it,i search on. I searched over internet,i but I haven't found that Amelia packagesthe Amelia package can impute missing values; i used but it giving error,how can i resolve itvalues.

library(Amelia) I used it as follows:

t<-read.table("C:\\Users\\exam\\Desktop\\missing_ts.txt")

library(Amelia)
t <- read.table("C:\\Users\\exam\\Desktop\\missing_ts.txt")
a.out <- amelia(t)

> a.out <- amelia(t) but I got the following error:

Amelia Error Code: 42 There is only 1 column of data. Cannot impute

Amelia Error Code:  42 
There is only 1 column of data. Cannot impute

> amelia(x=as.matrix(1:101,t$V1)) Likewise,

amelia(x=as.matrix(1:101,t$V1))

Amelia Error Code: 39 Your data has no missing values. Make sure the code for missing data is set to the code for R, which is NA. resulted in

Amelia Error Code:  39 
Your data has no missing values.  Make sure the code for 
missing data is set to the code for R, which is NA.

is my way of predictingIs there something wrong in the way I'm trying to forecast this time series?,if If yes,then which then what method should i followI use?

how to predict missing values in time series?

i have time series as

in which NA are missing values,i want to predict/forecast it,i search on internet,i found that Amelia packages can impute missing values; i used but it giving error,how can i resolve it

library(Amelia)

t<-read.table("C:\\Users\\exam\\Desktop\\missing_ts.txt")

> a.out <- amelia(t)

Amelia Error Code: 42 There is only 1 column of data. Cannot impute

> amelia(x=as.matrix(1:101,t$V1))

Amelia Error Code: 39 Your data has no missing values. Make sure the code for missing data is set to the code for R, which is NA.

is my way of predicting wrong?,if yes,then which method should i follow?

How to predict missing values in time series?

I have the following time series

in which NA are missing values, and I want to predict/forecast it. I searched over internet, but I haven't found that the Amelia package can impute missing values.

I used it as follows:

library(Amelia)
t <- read.table("C:\\Users\\exam\\Desktop\\missing_ts.txt")
a.out <- amelia(t)

but I got the following error:

Amelia Error Code:  42 
There is only 1 column of data. Cannot impute

Likewise,

amelia(x=as.matrix(1:101,t$V1))

resulted in

Amelia Error Code:  39 
Your data has no missing values.  Make sure the code for 
missing data is set to the code for R, which is NA.

Is there something wrong in the way I'm trying to forecast this time series? If yes, then what method should I use?

added 18 characters in body; added 2 characters in body; added 50 characters in body
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Sagar Nikam
Sagar Nikam
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i have time series as   

1.3578511 0.5119648 1.3189847 0.9214787 1.2272616 4.9167998 1.2272616 1.2272616 0.8854192 2.3386331 1.6132899 0.2030302 0.8426226 1.2277843 NA 1.3189847 1.3578511 0.8530141 2.3386331 1.0541099 0.7747481 0.5764672 1.3189847 1.2160533 1.2272616 0.6715839 0.9651803 1.6132899 1.2006974 0.6875047 1.3245534 1.2006974 0.8221709 1.3101684 1.6132899 1.6132899 1.2006974 1.3189847 1.0018480 1.2277843 1.4424190 1.6132899 1.2277843 1.2006974 0.7779642 0.9381081 0.8854192 NA NA 1.3189847 1.1070461 0.8221709 4.9167998 0.9214787 1.3189847 1.3189847 1.2277843 1.4424190 1.6132899 1.6132899 4.9167998 0.8235792 0.9708839 1.1070461 1.2160533 0.8354292 1.4424190 1.1958634 0.5119648 1.4424190 1.4424190 1.6132899 1.6132899 0.6710844 1.2272616 0.9708839 0.8890464 1.4424190 0.8890464 0.8221709 1.1958634 0.8132233 0.4630722 4.9167998 0.8890464 1.3189847 0.7373181 1.1070461 1.2279813 0.8890464 0.3588158 1.4424190 0.8132233 0.4297043 1.3578511 4.9167998 1.2272616 0.8426226 1.4424190 1.6132899 NA in

in which NA are missing values,i want to predict/forecast it,i search on internet,i found that Amelia packages can impute missing valuesvalues; i used but it giving error,how can i resolve it

`library(Amelia) t<-read.table("C:\Users\exam\Desktop\missing_ts.txt")library(Amelia)

a.out <- amelia(t) Amelia Error Code: 42 There is only 1 column of data. Cannot impute. amelia(x=as.matrix(1:101,t$V1)) Amelia Error Code: 39 Your data has no missing values. Make sure the code for missing data is set to the code for R, which is NA. `

t<-read.table("C:\\Users\\exam\\Desktop\\missing_ts.txt")

> a.out <- amelia(t)

Amelia Error Code: 42 There is only 1 column of data. Cannot impute

> amelia(x=as.matrix(1:101,t$V1))

Amelia Error Code: 39 Your data has no missing values. Make sure the code for missing data is set to the code for R, which is NA.

is my way of predicting wrong?,if yes,then which method should i follow?

i have time series as  1.3578511 0.5119648 1.3189847 0.9214787 1.2272616 4.9167998 1.2272616 1.2272616 0.8854192 2.3386331 1.6132899 0.2030302 0.8426226 1.2277843 NA 1.3189847 1.3578511 0.8530141 2.3386331 1.0541099 0.7747481 0.5764672 1.3189847 1.2160533 1.2272616 0.6715839 0.9651803 1.6132899 1.2006974 0.6875047 1.3245534 1.2006974 0.8221709 1.3101684 1.6132899 1.6132899 1.2006974 1.3189847 1.0018480 1.2277843 1.4424190 1.6132899 1.2277843 1.2006974 0.7779642 0.9381081 0.8854192 NA NA 1.3189847 1.1070461 0.8221709 4.9167998 0.9214787 1.3189847 1.3189847 1.2277843 1.4424190 1.6132899 1.6132899 4.9167998 0.8235792 0.9708839 1.1070461 1.2160533 0.8354292 1.4424190 1.1958634 0.5119648 1.4424190 1.4424190 1.6132899 1.6132899 0.6710844 1.2272616 0.9708839 0.8890464 1.4424190 0.8890464 0.8221709 1.1958634 0.8132233 0.4630722 4.9167998 0.8890464 1.3189847 0.7373181 1.1070461 1.2279813 0.8890464 0.3588158 1.4424190 0.8132233 0.4297043 1.3578511 4.9167998 1.2272616 0.8426226 1.4424190 1.6132899 NA in which NA are missing values,i want to predict/forecast it,i search on internet,i found that Amelia packages can impute missing values

`library(Amelia) t<-read.table("C:\Users\exam\Desktop\missing_ts.txt")

a.out <- amelia(t) Amelia Error Code: 42 There is only 1 column of data. Cannot impute. amelia(x=as.matrix(1:101,t$V1)) Amelia Error Code: 39 Your data has no missing values. Make sure the code for missing data is set to the code for R, which is NA. `

is my way of predicting wrong?,if yes,then which method should i follow?

i have time series as 

1.3578511 0.5119648 1.3189847 0.9214787 1.2272616 4.9167998 1.2272616 1.2272616 0.8854192 2.3386331 1.6132899 0.2030302 0.8426226 1.2277843 NA 1.3189847 1.3578511 0.8530141 2.3386331 1.0541099 0.7747481 0.5764672 1.3189847 1.2160533 1.2272616 0.6715839 0.9651803 1.6132899 1.2006974 0.6875047 1.3245534 1.2006974 0.8221709 1.3101684 1.6132899 1.6132899 1.2006974 1.3189847 1.0018480 1.2277843 1.4424190 1.6132899 1.2277843 1.2006974 0.7779642 0.9381081 0.8854192 NA NA 1.3189847 1.1070461 0.8221709 4.9167998 0.9214787 1.3189847 1.3189847 1.2277843 1.4424190 1.6132899 1.6132899 4.9167998 0.8235792 0.9708839 1.1070461 1.2160533 0.8354292 1.4424190 1.1958634 0.5119648 1.4424190 1.4424190 1.6132899 1.6132899 0.6710844 1.2272616 0.9708839 0.8890464 1.4424190 0.8890464 0.8221709 1.1958634 0.8132233 0.4630722 4.9167998 0.8890464 1.3189847 0.7373181 1.1070461 1.2279813 0.8890464 0.3588158 1.4424190 0.8132233 0.4297043 1.3578511 4.9167998 1.2272616 0.8426226 1.4424190 1.6132899 NA

in which NA are missing values,i want to predict/forecast it,i search on internet,i found that Amelia packages can impute missing values; i used but it giving error,how can i resolve it

library(Amelia)

t<-read.table("C:\\Users\\exam\\Desktop\\missing_ts.txt")

> a.out <- amelia(t)

Amelia Error Code: 42 There is only 1 column of data. Cannot impute

> amelia(x=as.matrix(1:101,t$V1))

Amelia Error Code: 39 Your data has no missing values. Make sure the code for missing data is set to the code for R, which is NA.

is my way of predicting wrong?,if yes,then which method should i follow?

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Sagar Nikam
Sagar Nikam
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