I would like to calculate rolling yearly differences based on a daily time series in R with a xts object. However, I see currently two issues:
The number of trading days per year is not constant.
There could be holes in the time series, e.g. one year could be missing in-between.
Are there functions available in the library to take such rolling differences without constant lags (e.g. a lag of 260 days could be off by 10 days sometimes)? Or would the correct approach here to search for each date the same date one year before (minus one or two days to account for weekends)?
Related
Are there tools in R that simplify analysis of lagged and disparate time series. For example:
Daily values that only occur on weekdays (no entry on weekends or holidays)
vs
Bi-annual values
What I'm seeking is ways to:
Complete the missing daily values (with interpolated, or last value rolled forward, etc.)
Look for correlation between daily values and the bi-annual value (only the values that came before the bi-annual event)
As an example:
10-year treasury note interest rate (daily on non-holiday weekdays) as "X" and i-bond fixed rate as "Y" (set May 1/Nov 1)
Any suggestions appreciated.
I've built a test dataset manually for "x" and used functions in zoo to populate the missing values (interpolated), but I'm hoping for a less "brute-force" method for looking at analyzing the disparate time series. I've used lag functions in the past, but those were on matching interval time series.
What Jon commented is what I had in mind:
expand a weekday time series to full week using missing value function(s) in zoo
Sample the daily value - say April 15 for the May 1, Oct 15 for Nov 1
Ideally be able to automate - say loop through April 1-30, Oct 1-30 to look for highest RSqr for the model of choice (linear, polynomial, etc.)
Not have to build discrete datasets for each of the above - but if that is what is required I can do it programmatically - I've done that with stock data in the past. I was looking for a more efficient means of selecting the datasets ad hoc during the analysis.
I don't have code to post, because I'm clueless as to the feature/function that would make the date selection I'm after possible (at least in R).
Thanks for the input so far. It has already been useful in helping me look at alternative methods to achieve what I'm after.
I have a relative large xts object. It contains the daily adjusted closing prices from 2012-2021 for each company in the STOXX 600 Europe. I want to calculate the yearly volatility of the stocks for each year for each company.
Here is a link of how my dataset look like:
https://imgur.com/a/oS7ROCL
So I started by calculate the log differences by:
XTS.LOGDIFFS <- diff(log(XTS.ADJCLOSE))
The next step would be to calculate the volatility of the stocks for a specific period of time, for example from 2012-05-01 till 2012-12-31, by using the standard deviation and multiply it with the square root of 252 ( 252 are the average trading days).
So my idea is this: First I want to extract the data from my dataset for a specific period of time, in this case from 2012-05-01 till 2012-12-31.
I tried this XT1<-xts(XTS.LOGDIFFS [1:174]).
As an alternativ I have thought about this:
start_date<- as.Date("2012-05-01")
end_date<-as.Date("2012-12-31")
The next step would be to calculate the volatility for the extracted xts object.
So I tried this: vol<-sd(XTS.1, na.rm = TRUE) *sqrt (252).
But this only give me the stock volatility for all combined and not for every single one.
So I think I need a function to get the stock volatility for every company for the extracted period of time in the xts object. But I have no clue how this should look like.
I am trying to plot a decomposed time series, but running into an error:
Error in decompose(ts_ret) : time series has no or less than 2 periods`.
I am forcing the time series to a fixed period that is higher than 2.
Why does the ts think the period is less than 2?
Shouldn't the period be set automatically based on the time intervals in the data? (which are daily)
rm(list=ls())
library(jsonlite)
library(xts)
item.id<-18
eve.url<-paste0("http://eve-marketdata.com/api/item_history2.json?char_name=demo®ion_ids=10000002&type_ids=",item.id,"&days=100")
eve.data<-data.frame(fromJSON(txt=eve.url))$emd.row
eve.data$date<-as.POSIXct(eve.data$date,format="%Y-%m-%d",tz="EST")
xxx<-xts(as.numeric(eve.data[,"avgPrice"]),eve.data$date)
colnames(xxx)<-"trit"
ts_ret<-ts(xxx,frequency=52) #but Im setting the periods here.....
plot(decompose(ts_ret))
As #ufelder pointed out my dataset was too small to look at seasonal decomposition because I only had a few months of data (measured hourly), but not an entire seasons worth (which is 4 months). To fix this I had to modify the period of the dataset to once per day by using ts(xxx,frequency=365) so decompose would compare across days, not seasons.
I'm having trouble doing time series for my data set. Most examples have quarterly or monthly frequencies but my issue comes with data that is collect annually or every two years. Consider my code:
data<-data.frame(year=seq(1978,2012,2), number=runif(18,100,500))
time<-ts(data$number, start=1978, frequency=.5)
decomp<-decompose(time)
Error in decompose(time) : time series has no or less than 2 periods
How do I make R recognize time series values from data that is collected over an annual basis? Thanks!
Seasonal decomposition only makes sense with intra-yearly data, because you have seasons within years. So, trying to calculate seasonal effects with decompose on data collected every two years you get the error.
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.