My data contains several measurements in one day. It is stored in CSV-file and looks like this:
enter image description here
The V1 column is factor type, so I'm adding a extra column which is date-time -type: vd$Vdate <- as_datetime(vd$V1) :
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Then I'm trying to convert the vd-data into time series: vd.ts<- ts(vd, frequency = 365)
But then the dates are gone:
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I just cannot get it what I am doing wrong! Could someone help me, please.
Your dates are gone because you need to build the ts dataframe from your variables (V1, ... V7) disregarding the date field and your ts command will order R to structure the dates.
Also, I noticed that you have what is seems like hourly data, so you need to provide the frequency that is appropriate to your time not 365. Considering what you posted your frequency seems to be a bit odd. I recommend finding a way to establish the frequency correctly. For example, if I have hourly data for 365 days of the year then I have a frequency of 365.25*24 (0.25 for the leap years).
So the following is just as an example, it still won't work properly with what I see (it is limited view of your dataset so I am not sure 100%)
# Build ts data (univariate)
vs.ts <- ts(vd$V1, frequency = 365, start = c(2019, 4)
# check to see if it is structured correctly
print(vd.ts, calendar = T)
Finally my time series is working properly. I used
ts <- zoo(measurements, date_times)
and I found out that the date_times was supposed to be converted with as_datetime() as otherwise they were character type. The measurements are converted into data.frame type.
Related
i have a WEEKLY dataset that start on 1986.01.03 and end on 2022-10-07.
The problem is when I forecast the time series with Arima +garch, because the date in T0 is wrong, i.e. 1975 enter image description here.
The function that I used to convert the dataset into time series is here, but I think that the problem is here, since it doesn't take on the right date.
FutureWeekly= ts(WeeklyFuture$FutureWeekly, start= c(1986,1), end = c(2022,10), frequency = 52)
does anyone know how to convert a weekly dataset to time series other than this?
There are the first rows of my dataset and then I have to transform that into returns (diff(log(FutureWeekly) to do the ARMA+GARCH
enter image description here
Try this:
futures<-c(WeeklyFuture$FutureWeekly) #convert to vector
FutureWeekly= ts(futures, start= c(1986,1,10), end = c(1986,3,7), frequency = 52) #add day of week ending on
One of the things ts() demands is a vector of values. I think it might also be easier for ts() to convert the data if it was able to see the 7-day increments.
Assuming you have full un-broken weekly data for the entire period, I think these two things will solve the problem.
I have a daily rainfall data for 36 years. I want to analyze the time series, but my data is still in the form of frame data, how I change the frame data into time series. My data is a variable, how to unify the year number with the date and month, so the data is only in one column
You could use a time series package for that, such as fpp i.e. install.packages('fpp'). Since you don't give an example code, I can't really help you properly with it but it's quite easy.
ts(your_data, start =, frequency = ) At start = you put the year or month where you'd start and at frequency = you'd put e.g. 36 since you talk about 36 years.
You might want to check out https://robjhyndman.com/. He has an online (free) book available that walks you through the use of his package as well as providing useful information with respect to time series analysis.
Hope this helps.
I want to get a panel data set into zoo so that it catches both month and year. My data set looks like this.
and the data can be downloaded from HERE.
The best way I could do is,
dat<-read.csv("dat_lag.csv")
zdat <- read.zoo(dat, format="%d/%m/%Y")
However, I could do this by including column 1- Date and column 4- Day in my data set. Is there any clever way to get both month and year into zoo using R without including the Date and Day columns? Thanks, in advance for any help.
I have a 3000 x 1000 matrix time series database going back 14 years that is updated every three months. I am forecasting out 9 months using this data still keeping a 3200 x 1100 matrix (mind you these are rough numbers).
During the forecasting process I need the variables Year and Month to be calculated appropriately . I am trying to automate the process so I don't have to mess with the code any more; I can just run the code every three months and upload the projections into our database.
Below is the code I am using right now. As I said above I do not want to have to look at the data or the code just run the code every three months. Right now everything else is working as planed, but I still have to ensure the dates are appropriately annotated. The foo variables are changed for privacy purposes due to the nature of their names.
projection <- rbind(projection, data.frame(foo=forbar, bar=barfoo,
+ Year=2012, Month=1:9,
+ Foo=as.vector(fc$mean)))
I'm not sure exactly where the year/months are coming from, but if you want to refer to the current date for those numbers, here is an option (using the wonderful package, lubridate):
library(lubridate)
today = Sys.Date()
projection <- rbind(projection, data.frame(foo=foobar, bar=barfoo,
year = year(today),
month = sapply(1:9,function(x) month(today+months(x))),
Foo = as.vector(fc$mean)))
I hope this is what you're looking for.
I've been trying to do a time series on my dataframe, and I need to strip times from my csv. This is what I've got:
campbell <-read.csv("campbell.csv")
campbell$date = strptime(campbell$date, "%m/%d")
campbell.ts <- xts(campbell[,-1],order.by=campbell[,1])
First, what I'm trying to do is just get xts to strip the dates as "xx/xx" meaning just the month and day. I have no year for my data. When I try that second line of code and call upon the date column, it converts it to "2013-xx-xx." These months and days have no year associated with them, and I can't figure out how to get rid of the 2013. (The csv file I'm calling on has the dates in the format "9/30,10/1...etc.)
Secondly, once I try and make a time series (the third line), I am unsure what the "order.by" command is calling on. What am I indexing?
Any help??
Thanks!
For strptime, you need to provide the full date, i.e. day, month and year. In case, any of these is not provided, current ones are assumed from the system's time and appended to the incomplete date. So, if you want to retain your date format as you have read it, first make a copy of that and store in a temporary variable and then use strptime over campbell$date to convert into R readable date format. Since, year is not a concern to you, you need not bother about it even though it is automatically appended by strptime.
campbell <-read.csv("campbell.csv")
date <- campbell$date
campbell$date <- strptime(campbell$date, "%m/%d")
Secondly, what you are doing by 'the third line' (xts(campbell[,-1],order.by=campbell[,1])) command is that, your are telling to order all the data of campbell except the first column (campbell[,-1]) according to the index provided by the time data in the first column of campbell (campbell[,1]). So, it would only work given the date is in the first column.
After ordering the data according to time-series, you can replace back the campbell$date column with date to get back the date format you wanted (although here, first you have to order date also like shown below)
date <- xts(date, order.by=campbell[,1]) # assuming campbell$date is campbell[,1]
campbell.ts <- xts(campbell[,-1], order.by=campbell[,1])
campbell.ts <- cbind(date, campbell.ts)
format(as.Date(campbell$dat, "%m/%d/%Y"), "%m/%d")