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.
Related
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) :
enter image description here
Then I'm trying to convert the vd-data into time series: vd.ts<- ts(vd, frequency = 365)
But then the dates are gone:
enter image description here
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$V1, frequency = 365, start = c(2019, 4)
# check to see if it is structured correctly
print(vd.ts, calendar = T)
Finally my time series is working properly. I used
ts <- zoo(measurements, date_times)
and I found out that the date_times was supposed to be converted with as_datetime() as otherwise they were character type. The measurements are converted into data.frame type.
I have a dataset having solar power generation for 24 hours for many days, now I have to find the average of the power generated in accordance with the time, as for example, Have a glimpse of the datasetI have to find the average of the power generated at time 9:00:00 AM.
Start by stripping out the time from the date-time variable.
Assuming your data is called myData
library(lubridate)
myData$Hour <- hour(strptime(myData$Time, format = "%Y-%m-%d %H:%M:%S"))
Then use ddply from the plyr package, which allows us to apply a function to a subset of data.
myMeans <- ddply(myData[,c("Hour", "IT_solar_generation")], "Hour", numcolwise(mean))
The resulting frame will have one column called Time which will give you the hour, and another with the means at each hour.
NOW, on another side but important note, when you ask a question you should be providing information on the attempts you've made so far to answer the question. This isn't a help desk.
I have 2 years of hourly data.I want to check seasonality .
1.Decomposing the series shows seasonality.But since Decomposition is not enough
what else can i use to check seasonality in R?
2.I tried hourly seasonality , I am not sure on the period of seasonality.How to determine the frequency in R?
Frequency is the number of observations per unit of time. But, in my short experience, the unit of time depends on the event you are studying. For example, if you have monthly data of a yearly seasonal event (like the flowering of some plants) and you sampled 5 times each month, frequency will be 5*12. I suggest you decompose your time series and and check for seasonality there. You can use ts, stl and plot.stl. Try to adjust the parameters as best as you can but also try to check what happens when you change them.
Please read through below link, if you feel to keep multiple seasonal periods in data, you can also paste sample of your data here for further suggestions
https://robjhyndman.com/hyndsight/seasonal-periods/
How does the ts() function use its frequency parameter? What is the effect of assigning wrong values as frequency?
I am trying to use 1.5 years of website usage data to build a time series model so that I can forecast the usage for coming periods. I am using data at daily level. What should be the frequency here - 7 or 365 or 365.25?
The frequency is "the" period at which seasonal cycles repeat. I use "the" in scare quotes since, of course, there are often multiple cycles in time series data. For instance, daily data often exhibit weekly patterns (a frequency of 7) and yearly patterns (a frequency of 365 or 365.25 - the difference often does not matter).
In your case, I would assume that weekly patterns dominate, so I would assign frequency=7. If your data exhibits additional patterns, e.g., holiday effects, you can use specialized methods accounting for multiple seasonalities, or work with dummy coding and a regression-based framework.
Here, the frequency parameter is not a frequency that you can observe in the data of your time series. Instead, you have to specify the frequency at which samples of the time series were taken. In your case, this is simply 1 day, or 1.
The value you give here will influence the results you get later when running analysis operations (examples are average requests per time unit or fourier transformation to get the (real) frequencies in the data). E.g. if you wanted to get all your results in the unit of hours instead of in days, you would pass 24 instead of 1 as frequency, because your data samples were taken in a frequency of 24 hours.
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.