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.
Related
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.
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 number of data points that I am trying to extract a meaningful pattern from (or derive an equation that could then be predictive). I am trying to find a correlation (?) between RANK and DAILY SALES for any given ITEM.
So, for any given item, I have (say) two weeks of daily information, each day consists of a pairing of Inventory, and Rank.
ITEM #1
Monday: 20 in stock (rank 30)
Tuesday: 17 in stock (rank 29)
Wednesday: 14 in stock (rank 31)
The presumption is that 3 items were sold each day, and that selling ~3 a day is roughly what it means to have a rank of ~30.
Given information like this across a wide span (20,000 items, over 2 weeks) of inventory/rank/date pairings, I'd like to derive an equation/method of estimating what the daily sales would be for any given rank.
There's one problem:
The data isn't entirely clean, because -occasionally- the inventory fluctuates upward, either because of re-stocking, or because of returns. So for example, you might see something like
MONDAY: 30 in stock.
TUESDAY: 20 in stock.
WEDNESDAY: 50 in stock.
THURSDAY: 40 in stock.
FRIDAY: 41 in stock.
Indicating that, between Tuesday and wednesday, 30 more were replenished, and on thursday, one was returned.
I am planning to use mean and standard deviation on Daily sales for given rank.
So if any rank given I can predict the daily sales based on mean and standard deviation values.
Is this correct approach? IS there any better approach for this scenario
Sounds like this could be a good read for you, fpp
It provides an introduction to timeseries forecasting. Timeseries forecasting
has a lot of nuance so it can trip people up pretty easily. Some of the issues
you have already noted (e.g. seasonality). Others pertain to the statistical
properties of such series of data. Take a look through this and
I have two time series.
Each point in either time series is for a week. A week here is not exactly a calendar week, but the first week in a calendar year always starts from Jan 1, and the other weeks in the same year follow that, and the last week of the year may contain more than 7 days but no more than 13 days.
The first time series A is stored in a compressed (.gz) text file A.gz, which looks like (each week and the corresponding time series value are separated by a comma in a line):
week,value
20060101-20060107,0
20060108-20060114,5
...
20061217-20061223,0
20061224-20061230,0
20070101-20070107,0
20070108-20070114,4
...
20150903-20150909,0
20150910-20150916,1
The second time series B is similarly stored in a compressed (.gz) text file B.gz, but over a subset of period of A, which looks like:
week,value
20130122-20130128,509
20130129-20130204,204
...
20131217-20131223,150
20131224-20131231,148.0
20140101-20140107,365.0
20140108-20140114,45.0
...
20150305-20150311,0
20150312-20150318,364
I wonder how to calculate the cross correlation between the two time series A and B (up to a specified maximum lag), and plot A and B in a single plot, in R?
Thanks
My data is memory consumption of an application for every 10 minute interval for the last 26 days.My start date is Oct 6th 2013 and end date is Novemeber 2nd 2013.I've read the data in to a time frame and cleaned it up. Now am trying to create a time series , something along the lines of my_ts<-ts(mydata[3],start=c(2013,10),frequency=10)
Am sure this not correct as the frequency , can someone point me in the right direction so I can plot the time series
.
In R, frequency actually means the period of the seasonality. i.e., frequency = frequency of observations per season. In your case, the "season" is presumably one day. So you want
ts(mydata[3],start=c(2013,10),frequency=24*60/10)