xts | 易学教程

Creating regular 15-minute time-series from irregular time-series

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I have an irregular time-series (with DateTime and RainfallValue) in a csv file C:\SampleData.csv : DateTime,RainInches 1/6/2000 11:59,0 1/6/2000 23:59,0.01 1/7/2000 11:59,0 1/13/2000 23:59,0 1/14/2000 0:00,0 1/14/2000 23:59,0 4/14/2000 3:07,0.01 4/14/2000 3:12,0.03 4/14/2000 3:19,0.01 12/31/2001 22:44,0 12/31/2001 22:59,0.07 12/31/2001 23:14,0 12/31/2001 23:29,0 12/31/2001 23:44,0.01 12/31/2001 23:59,0.01 Note: The irregular time-steps could be 1 min, 15 min, 1 hour, etc. Also, there could be multiple observations in a desired 15-min interval. I am trying to create a regular 15-minute time

Convert data frame with date column to timeseries

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I've got a data frame with the following data: >PRICE DATE CLOSE 1 20070103 54.700 2 20070104 54.770 3 20070105 55.120 4 20070108 54.870 5 20070109 54.860 6 20070110 54.270 7 20070111 54.770 8 20070112 55.360 9 20070115 55.760 ... As you can see my DATE column represents a date (yyyyMMdd) and my CLOSE column represents prices. I now have to calculate CalmarRatio, from the PerformanceAnalytics package. I'm new to R, so i can't understand everything, but from what i have googled to the moment i see that the R parameter to that function needs to be a time-series-like object. Is there any way i

R xts: generating 1 minute time series from second events

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问题 I have an xts sequence of stock trade events that I want to process to generate 1 minute OHLC time series. For instance this set of trades: Timestamp Price Size 9:30:00.123 12.32 200 9:30.00.532 12.21 100 9:30.32.352 12.22 500 9:30.45.342 12.35 200 Should result in the 9:30:00 record: Timestamp Open High Low Close 9:30:00 12.32 12.35 12.21 12.35 The way I approached this is to split the original trade series by the minute: myminseries = do.call(rbind, lapply(split(mytrades, "minutes"),

Return data subset time frames within another timeframes?

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There are very nifty ways of subsetting xts objects. For example, one can get all the data for all years, months, days but being strictly between 9:30 AM and 4 PM by doing: my_xts["T09:30/T16:00"] Or you can get all the observations between two dates by doing: my_xts["2012-01-01/2012-03-31"] Or all the dates before/after a certain date by doing: my_xts["/2011"] # from start of data until end of 2011 my_xts["2011/"] # from 2011 until the end of the data How can I get all the data for only certain months for all years or only certain days for all months and years? Do any other subsetting tricks

Access zoo or xts index

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问题 I am using zoo objects, buy my question also applies to xts objects. It looks to me like it is a one column vector with an index. In my case the index is the vector of dates and the one column vector my data. All is good except that I would like to access the dates (from the index). For example I have the following result: ObjZoo <- structure(c(10, 20), .Dim = c(2L, 1L), index = c(14788, 14789), class = "zoo", .Dimnames = list(NULL, "Data")) unclass(ObjZoo) # Data # [1,] 10 # [2,] 20 # attr(,

Rolling Sum by Another Variable in R

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问题 I want to get the rolling 7-day sum by ID. Suppose my data looks like this: data<-as.data.frame(matrix(NA,42,3)) data$V1<-seq(as.Date("2014-05-01"),as.Date("2014-09-01"),by=3) data$V2<-rep(1:6,7) data$V3<-rep(c(1,2),21) colnames(data)<-c("Date","USD","ID") Date USD ID 1 2014-05-01 1 1 2 2014-05-04 2 2 3 2014-05-07 3 1 4 2014-05-10 4 2 5 2014-05-13 5 1 6 2014-05-16 6 2 7 2014-05-19 1 1 8 2014-05-22 2 2 9 2014-05-25 3 1 10 2014-05-28 4 2 How can I add a new column that will contain the rolling

Which is the best method to apply a script repetitively to n .csv files in R?

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My situation : I have a number of csv files all with the same suffix pre .csv, but the first two characters of the file name are different (ie AA01.csv, AB01.csv, AC01.csv etc) I have an R script which I would like to run on each file. This file essentially extracts the data from the .csv and assigns them to vectors / converts them into timeseries objects. (For example, AA01 xts timeseries object, AB01 xts object) What I would like to achieve : Embed the script within a larger loop (or as appropriate) to sequentially run over each file and apply the script Remove the intermediate objects

Rolling window over irregular time series

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问题 I have an irregular time series of events (posts) using xts , and I want to calculate the number of events that occur over a rolling weekly window (or biweekly, or 3 day, etc). The data looks like this: postid 2010-08-04 22:28:07 867 2010-08-04 23:31:12 891 2010-08-04 23:58:05 901 2010-08-05 08:35:50 991 2010-08-05 13:28:02 1085 2010-08-05 14:14:47 1114 2010-08-05 14:21:46 1117 2010-08-05 15:46:24 1151 2010-08-05 16:25:29 1174 2010-08-05 23:19:29 1268 2010-08-06 12:15:42 1384 2010-08-06 15:22

Regular analysis over irregular time series

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问题 I have an irregular time series ( xts in R ) that I want to apply some time-windowing to. For example, given a time series like the following, I want to compute things like how many observations there are in each discrete 3-hour window, starting from 2009-09-22 00:00:00 : library(lubridate) s <- xts(c("OK", "Fail", "Service", "OK", "Service", "OK"), ymd_hms(c("2009-09-22 07:43:30", "2009-10-01 03:50:30", "2009-10-01 08:45:00", "2009-10-01 09:48:15", "2009-11-11 10:30:30", "2009-11-11 11:12:45

Forecasting time series data

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I've done some research and I am stuck in finding the solution. I have a time series data, very basic data frame, let's call it x : Date Used 11/1/2011 587 11/2/2011 578 11/3/2011 600 11/4/2011 599 11/5/2011 678 11/6/2011 555 11/7/2011 650 11/8/2011 700 11/9/2011 600 11/10/2011 550 11/11/2011 600 11/12/2011 610 11/13/2011 590 11/14/2011 595 11/15/2011 601 11/16/2011 700 11/17/2011 650 11/18/2011 620 11/19/2011 645 11/20/2011 650 11/21/2011 639 11/22/2011 620 11/23/2011 600 11/24/2011 550 11/25/2011 600 11/26/2011 610 11/27/2011 590 11/28/2011 595 11/29/2011 601 11/30/2011 700 12/1/2011 650 12