Convert multiple ID's weekly data to time series format
0
Here is the structure of my data
structure(list(customer_id = c("A", "A", "A", "A", "A", "A",
"A", "A", "B", "B", "B", "B", "B"), state = c("NC", "NC", "NC",
"NC", "NC", "NC", "NC", "NC", "KA", "KA", "KA", "KA", "KA"),
value = c(20.4, 29, 26, 40, 35, 36, 28, 41, 70, 75, 78, 99,
40), Date = structure(c(17784, 17791, 17798, 17805, 17812,
17819, 17826, 17833, 17608, 17615, 17622, 17629, 17636), class = "Date")), row.names = c(NA,
-13L), class = "data.frame")
I have so many customers of data from different state with different start and end dates.
I'd like to convert this data to time_series data and perform exponential smoothing using hw method.
The code which I have tried to convert to time_series is:
temp <- multiple_ts %>%
group_by(state,customer_id) %>%
ts(multiple_ts$value, frequency = 52)
I used frequency as 52 because the data is weekly_data,but the code is throwing an error
Warning messages:
1: In data.matrix(data) : NAs introduced by coercion
2: In data.matrix(data) : NAs introduced by coercion
my output_data is
structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20.4, 29,
26, 40, 35, 36, 28, 41, 70, 75, 78, 99, 40, 17784, 17791, 17798,
17805, 17812, 17819, 17826, 17833, 17608, 17615, 17622, 17629,
17636), .Dim = c(13L, 4L), .Dimnames = list(NULL, c("customer_id",
"state", "value", "Date")), .Tsp = c(20.9384615384615, 21.1692307692308,
52), class = c("mts", "ts", "matrix"))
Can someone help me in R.
Thanks in Advance
r dplyr time-series smoothing exponential
asked Nov 12 '18 at 2:49
Lalitha
106
|
show 3 more comments
0
Here is the structure of my data
structure(list(customer_id = c("A", "A", "A", "A", "A", "A",
"A", "A", "B", "B", "B", "B", "B"), state = c("NC", "NC", "NC",
"NC", "NC", "NC", "NC", "NC", "KA", "KA", "KA", "KA", "KA"),
value = c(20.4, 29, 26, 40, 35, 36, 28, 41, 70, 75, 78, 99,
40), Date = structure(c(17784, 17791, 17798, 17805, 17812,
17819, 17826, 17833, 17608, 17615, 17622, 17629, 17636), class = "Date")), row.names = c(NA,
-13L), class = "data.frame")
I have so many customers of data from different state with different start and end dates.
I'd like to convert this data to time_series data and perform exponential smoothing using hw method.
The code which I have tried to convert to time_series is:
temp <- multiple_ts %>%
group_by(state,customer_id) %>%
ts(multiple_ts$value, frequency = 52)
I used frequency as 52 because the data is weekly_data,but the code is throwing an error
Warning messages:
1: In data.matrix(data) : NAs introduced by coercion
2: In data.matrix(data) : NAs introduced by coercion
my output_data is
structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20.4, 29,
26, 40, 35, 36, 28, 41, 70, 75, 78, 99, 40, 17784, 17791, 17798,
17805, 17812, 17819, 17826, 17833, 17608, 17615, 17622, 17629,
17636), .Dim = c(13L, 4L), .Dimnames = list(NULL, c("customer_id",
"state", "value", "Date")), .Tsp = c(20.9384615384615, 21.1692307692308,
52), class = c("mts", "ts", "matrix"))
Can someone help me in R.
Thanks in Advance
r dplyr time-series smoothing exponential
asked Nov 12 '18 at 2:49
Lalitha
106
1
Those aren't errors, they're warnings. There might be nothing wrong except you haveNAvalues.
– thelatemail
Nov 12 '18 at 2:52
I edited the question and posted with output.Can you help me to overcome this problem and get a time_series data and apply exponential smoothing method in R@thelatemail
– Lalitha
Nov 12 '18 at 3:05
Trying to combinetsanddplyr'sgroup_bydoesn't seem to work. You end up with a malformedmatrix. As per this question, you might be better using thextspackage's time-series - stackoverflow.com/questions/28489845/… . Or maybe revert to basic looping -lapply(split(multiple_ts$value, multiple_ts[c("state","customer_id")], drop=TRUE), ts, frequency=52)
– thelatemail
Nov 12 '18 at 3:24
I tried the basic looping but I didn't work with larger number of series of data,apart from this also when I tried for exponential_smoothing usinghwmethod it showed me asError in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped, : y should be a univariate time serieshow can we apply exponential series to multiple time_series of data.Can you help me@thelatemail
– Lalitha
Nov 12 '18 at 3:59
I'm not sure how you ended up with a multiple time series. The code I pasted abovesplitsmultiple_ts$value, which results in the required "univariate time series". Did you split the wholemultiple_tsormultiple_ts$value?
– thelatemail
Nov 12 '18 at 4:11
|
show 3 more comments
0
0
0
0
Here is the structure of my data
structure(list(customer_id = c("A", "A", "A", "A", "A", "A",
"A", "A", "B", "B", "B", "B", "B"), state = c("NC", "NC", "NC",
"NC", "NC", "NC", "NC", "NC", "KA", "KA", "KA", "KA", "KA"),
value = c(20.4, 29, 26, 40, 35, 36, 28, 41, 70, 75, 78, 99,
40), Date = structure(c(17784, 17791, 17798, 17805, 17812,
17819, 17826, 17833, 17608, 17615, 17622, 17629, 17636), class = "Date")), row.names = c(NA,
-13L), class = "data.frame")
I have so many customers of data from different state with different start and end dates.
I'd like to convert this data to time_series data and perform exponential smoothing using hw method.
The code which I have tried to convert to time_series is:
temp <- multiple_ts %>%
group_by(state,customer_id) %>%
ts(multiple_ts$value, frequency = 52)
I used frequency as 52 because the data is weekly_data,but the code is throwing an error
Warning messages:
1: In data.matrix(data) : NAs introduced by coercion
2: In data.matrix(data) : NAs introduced by coercion
my output_data is
structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20.4, 29,
26, 40, 35, 36, 28, 41, 70, 75, 78, 99, 40, 17784, 17791, 17798,
17805, 17812, 17819, 17826, 17833, 17608, 17615, 17622, 17629,
17636), .Dim = c(13L, 4L), .Dimnames = list(NULL, c("customer_id",
"state", "value", "Date")), .Tsp = c(20.9384615384615, 21.1692307692308,
52), class = c("mts", "ts", "matrix"))
Can someone help me in R.
Thanks in Advance
r dplyr time-series smoothing exponential
asked Nov 12 '18 at 2:49
Lalitha
106
Here is the structure of my data
structure(list(customer_id = c("A", "A", "A", "A", "A", "A",
"A", "A", "B", "B", "B", "B", "B"), state = c("NC", "NC", "NC",
"NC", "NC", "NC", "NC", "NC", "KA", "KA", "KA", "KA", "KA"),
value = c(20.4, 29, 26, 40, 35, 36, 28, 41, 70, 75, 78, 99,
40), Date = structure(c(17784, 17791, 17798, 17805, 17812,
17819, 17826, 17833, 17608, 17615, 17622, 17629, 17636), class = "Date")), row.names = c(NA,
-13L), class = "data.frame")
I have so many customers of data from different state with different start and end dates.
I'd like to convert this data to time_series data and perform exponential smoothing using hw method.
The code which I have tried to convert to time_series is:
temp <- multiple_ts %>%
group_by(state,customer_id) %>%
ts(multiple_ts$value, frequency = 52)
I used frequency as 52 because the data is weekly_data,but the code is throwing an error
Warning messages:
1: In data.matrix(data) : NAs introduced by coercion
2: In data.matrix(data) : NAs introduced by coercion
my output_data is
structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20.4, 29,
26, 40, 35, 36, 28, 41, 70, 75, 78, 99, 40, 17784, 17791, 17798,
17805, 17812, 17819, 17826, 17833, 17608, 17615, 17622, 17629,
17636), .Dim = c(13L, 4L), .Dimnames = list(NULL, c("customer_id",
"state", "value", "Date")), .Tsp = c(20.9384615384615, 21.1692307692308,
52), class = c("mts", "ts", "matrix"))
Can someone help me in R.
Thanks in Advance
r dplyr time-series smoothing exponential
r dplyr time-series smoothing exponential
asked Nov 12 '18 at 2:49
Lalitha
106
asked Nov 12 '18 at 2:49
Lalitha
106
edited Nov 12 '18 at 3:04
asked Nov 12 '18 at 2:49
Lalitha
106
asked Nov 12 '18 at 2:49
Lalitha
106
asked Nov 12 '18 at 2:49
Lalitha
106
106
1
Those aren't errors, they're warnings. There might be nothing wrong except you haveNAvalues.
– thelatemail
Nov 12 '18 at 2:52
I edited the question and posted with output.Can you help me to overcome this problem and get a time_series data and apply exponential smoothing method in R@thelatemail
– Lalitha
Nov 12 '18 at 3:05
Trying to combinetsanddplyr'sgroup_bydoesn't seem to work. You end up with a malformedmatrix. As per this question, you might be better using thextspackage's time-series - stackoverflow.com/questions/28489845/… . Or maybe revert to basic looping -lapply(split(multiple_ts$value, multiple_ts[c("state","customer_id")], drop=TRUE), ts, frequency=52)
– thelatemail
Nov 12 '18 at 3:24
I tried the basic looping but I didn't work with larger number of series of data,apart from this also when I tried for exponential_smoothing usinghwmethod it showed me asError in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped, : y should be a univariate time serieshow can we apply exponential series to multiple time_series of data.Can you help me@thelatemail
– Lalitha
Nov 12 '18 at 3:59
I'm not sure how you ended up with a multiple time series. The code I pasted abovesplitsmultiple_ts$value, which results in the required "univariate time series". Did you split the wholemultiple_tsormultiple_ts$value?
– thelatemail
Nov 12 '18 at 4:11
|
show 3 more comments
1
Those aren't errors, they're warnings. There might be nothing wrong except you haveNAvalues.
– thelatemail
Nov 12 '18 at 2:52
I edited the question and posted with output.Can you help me to overcome this problem and get a time_series data and apply exponential smoothing method in R@thelatemail
– Lalitha
Nov 12 '18 at 3:05
Trying to combinetsanddplyr'sgroup_bydoesn't seem to work. You end up with a malformedmatrix. As per this question, you might be better using thextspackage's time-series - stackoverflow.com/questions/28489845/… . Or maybe revert to basic looping -lapply(split(multiple_ts$value, multiple_ts[c("state","customer_id")], drop=TRUE), ts, frequency=52)
– thelatemail
Nov 12 '18 at 3:24
I tried the basic looping but I didn't work with larger number of series of data,apart from this also when I tried for exponential_smoothing usinghwmethod it showed me asError in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped, : y should be a univariate time serieshow can we apply exponential series to multiple time_series of data.Can you help me@thelatemail
– Lalitha
Nov 12 '18 at 3:59
I'm not sure how you ended up with a multiple time series. The code I pasted abovesplitsmultiple_ts$value, which results in the required "univariate time series". Did you split the wholemultiple_tsormultiple_ts$value?
– thelatemail
Nov 12 '18 at 4:11
1
1
Those aren't errors, they're warnings. There might be nothing wrong except you have
NA values.– thelatemail
Nov 12 '18 at 2:52
Those aren't errors, they're warnings. There might be nothing wrong except you have
NA values.– thelatemail
Nov 12 '18 at 2:52
I edited the question and posted with output.Can you help me to overcome this problem and get a time_series data and apply exponential smoothing method in R@thelatemail
– Lalitha
Nov 12 '18 at 3:05
I edited the question and posted with output.Can you help me to overcome this problem and get a time_series data and apply exponential smoothing method in R@thelatemail
– Lalitha
Nov 12 '18 at 3:05
Trying to combine
ts and dplyr's group_by doesn't seem to work. You end up with a malformed matrix. As per this question, you might be better using the xts package's time-series - stackoverflow.com/questions/28489845/… . Or maybe revert to basic looping - lapply(split(multiple_ts$value, multiple_ts[c("state","customer_id")], drop=TRUE), ts, frequency=52)– thelatemail
Nov 12 '18 at 3:24
Trying to combine
ts and dplyr's group_by doesn't seem to work. You end up with a malformed matrix. As per this question, you might be better using the xts package's time-series - stackoverflow.com/questions/28489845/… . Or maybe revert to basic looping - lapply(split(multiple_ts$value, multiple_ts[c("state","customer_id")], drop=TRUE), ts, frequency=52)– thelatemail
Nov 12 '18 at 3:24
I tried the basic looping but I didn't work with larger number of series of data,apart from this also when I tried for exponential_smoothing using
hw method it showed me as Error in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped, : y should be a univariate time series how can we apply exponential series to multiple time_series of data.Can you help me@thelatemail– Lalitha
Nov 12 '18 at 3:59
I tried the basic looping but I didn't work with larger number of series of data,apart from this also when I tried for exponential_smoothing using
hw method it showed me as Error in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped, : y should be a univariate time series how can we apply exponential series to multiple time_series of data.Can you help me@thelatemail– Lalitha
Nov 12 '18 at 3:59
I'm not sure how you ended up with a multiple time series. The code I pasted above
splits multiple_ts$value, which results in the required "univariate time series". Did you split the whole multiple_ts or multiple_ts$value?– thelatemail
Nov 12 '18 at 4:11
I'm not sure how you ended up with a multiple time series. The code I pasted above
splits multiple_ts$value, which results in the required "univariate time series". Did you split the whole multiple_ts or multiple_ts$value?– thelatemail
Nov 12 '18 at 4:11
|
show 3 more comments
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1
Those aren't errors, they're warnings. There might be nothing wrong except you have
NAvalues.– thelatemail
Nov 12 '18 at 2:52
I edited the question and posted with output.Can you help me to overcome this problem and get a time_series data and apply exponential smoothing method in R@thelatemail
– Lalitha
Nov 12 '18 at 3:05
Trying to combine
tsanddplyr'sgroup_bydoesn't seem to work. You end up with a malformedmatrix. As per this question, you might be better using thextspackage's time-series - stackoverflow.com/questions/28489845/… . Or maybe revert to basic looping -lapply(split(multiple_ts$value, multiple_ts[c("state","customer_id")], drop=TRUE), ts, frequency=52)– thelatemail
Nov 12 '18 at 3:24
I tried the basic looping but I didn't work with larger number of series of data,apart from this also when I tried for exponential_smoothing using
hwmethod it showed me asError in ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped, : y should be a univariate time serieshow can we apply exponential series to multiple time_series of data.Can you help me@thelatemail– Lalitha
Nov 12 '18 at 3:59
I'm not sure how you ended up with a multiple time series. The code I pasted above
splitsmultiple_ts$value, which results in the required "univariate time series". Did you split the wholemultiple_tsormultiple_ts$value?– thelatemail
Nov 12 '18 at 4:11