times booked_res
11:00 23
13:00 26
15:00 27
17:00 25
19:00 28
21:00 30
So I need to use the ts() function in R to convert this frame into a time series. The column on the right are the number of people reserved in each time. How should I approach this? I'm not sure about the arguments and I don't know if the frequency should be set to 24 (hours in a day) or 10 (11:00 to 21:00) as shown above. Any help appreciated.
First, find the frequency by noting that you are taking a sample every two minutes. The frequency is the inverse of the time between samples, which is 1/2 samples per minute or 30 samples per hour. The data you're interested in is on the right, so you can just use that data vector rather than the entire data frame. The code to convert that into a time series is simply:
booked_res <- c(23,26,27,25,28,30)
ts(booked_res,frequency = 30)
A simple plot with your data might be coded like this:
plot(ts(booked_res,frequency = 30),ylab='Number of people reserved',xlab='Time (in hours) since start of sampling',main='Time series chart of people reservations')
UPDATE:
A time series model can only be created in R when the times series is stationary. Using a varying sample rate would make the time series non-stationary, and so you wouldn't be able to create a time-series object in R.
This page on Analytics Vidhya provides a nice definition of stationary and non-stationary time series, while this page on R bloggers gives some resources that relate to analyzing a non-stationary time series.
Related
I am trying it hard to understand frequency setup in ts function using r when i have my time series dataset which is recorded every 15 mins between a time interval in a day which changes from day to day for example one day the data is recorded between 6 am till 9 am and another day it will be betwee 10am and 10 pm for 5 years. If this is the case, how do I set my frequency value in ts function? Please advise.
Raw Data
Thank you.
Regards,
Anil
This is my first question on stackoverflow, sorry if the question is poorly put.
I am currently developing a project where I predict how much a person drinks each day. I currently have data that looks like this:
The menge column represents how much water a person has actually drunk in 30 minutes (So first value represents amount from 8:00 till before 8:30 etc..). This is a 1 day sample from 3 months of data. The day starts at 8 AM and ends at 8 PM.
I am trying to forecast the Time Series for each day. For example, given the first one or two time steps, we would predict the whole day and then we know how much in total the person has drunk until 8 PM.
I am trying to model this data as a Time Series object in R (Google Colab), in order to use Croston's Method for the forecasting. Using the ts() function, what should I set the frequency to knowing that:
The data is half-hourly
The data is from 8:00 till 20:00 each day (Does not span the whole day)
Would I need to make the data span the whole day by adding 0 values? Are there maybe better approaches for this? Thank you in advance.
When using the ts() function, the frequency is used to define the number of (usually regularly spaced) observations within a given time period. For your example, your observations are every 30 minutes between 8AM and 8PM, and your time period is 1 day. The time period of 1 day assumes that the patterns over each day is of most interest here, you could also use 1 week here.
So within each day of your data (8AM-8PM) you have 24 observations (24 half hours). So a suitable frequency for this data would be 24.
You can also pad the data with 0 values, however this isn't necessary and would complicate the model. If you padded the data so that it has observations for all half-hours of the day, the frequency would then be 48.
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$Total_Sales_Spend, start=c(2017,1,1), frequency = 365)
However, when I try to decompose the TS, I am getting the following error:
time-series has no or less than 2 periods
I understand that it's because I don't have at least two years of data. So in this case what can I do?
Also, I'd like to forecast the daily sales numbers for the second half of October (16 days). How may I do that in this case? I wasn't able to set it up with zoo and arima.
Thank you.
Hi I'm new to timeseries so excuse I'm asking trivial questions, I have two questions:
I have hourly dataset (solar radiation) 7am - 7pm (12 hours for each day) what can I choose frequency for ts object?
I did this xts_2014.ts <- ts(xts_2014, frequency=12) but I think it recognize my data as monthly I know it's wrong but I don't know if 24 works when I don't have full 24 hours just daylight hours.
I have the raw data in black (which exhibits seasonal daily pattern and as far as I can tell yearly pattern), I tried moving average for removing noise of order 13 (hours) I don't know if I can proceed in trying to make data stationary?
plot
I am trying to plot a decomposed time series, but running into an error:
Error in decompose(ts_ret) : time series has no or less than 2 periods`.
I am forcing the time series to a fixed period that is higher than 2.
Why does the ts think the period is less than 2?
Shouldn't the period be set automatically based on the time intervals in the data? (which are daily)
rm(list=ls())
library(jsonlite)
library(xts)
item.id<-18
eve.url<-paste0("http://eve-marketdata.com/api/item_history2.json?char_name=demo®ion_ids=10000002&type_ids=",item.id,"&days=100")
eve.data<-data.frame(fromJSON(txt=eve.url))$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.