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$number, start=1978, frequency=.5)
decomp<-decompose(time)
Error in decompose(time) : time series has no or less than 2 periods
How do I make R recognize time series values from data that is collected over an annual basis? Thanks!
Seasonal decomposition only makes sense with intra-yearly data, because you have seasons within years. So, trying to calculate seasonal effects with decompose on data collected every two years you get the error.
Related
I have daily sales data between 1/1/2017 and 10/15/2017. My first question is that I was trying to set the time series with the following command:
marketing = ts(df$Total_Sales_Spend, start=c(2017,1,1), frequency = 365)
However, when I try to decompose the TS, I am getting the following error:
time-series has no or less than 2 periods
I understand that it's because I don't have at least two years of data. So in this case what can I do?
Also, I'd like to forecast the daily sales numbers for the second half of October (16 days). How may I do that in this case? I wasn't able to set it up with zoo and arima.
Thank you.
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)?
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/
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 have a data set for couple of years for prices wherein the prices are very low over the weekends as compared to weekdays.I want to load this as time series object in R. What could be the best approach so that I can capture the weekly seasonality (weekends price dips) of the data.
Thanks!
Use the ts function with frequency=7.