i have a data with date(2015)with mm/dd/yy format and sales. I need to predict sales for 2016 with the given data. I just know, I need to use time series forecasting. However no idea. Since, many examples have only year like(1960,1970,..) my data has only one year with several months. Don't know how to plot too. can you give me a clear structure how to proceed?
Assuming that the date is in string and in the format mm/dd/yy
convert string into date by using this code
a <- "07/23/15"
b <- as.Date(a, format = "%m/%d/%y")
fullYear <- format(b,'%Y') // to get 2015 as year
halfYear <- format(b, '%y') //to get 15 as year
After this you can work on
I have found the solution. Converted sales figure into time series format.
plotted the data and seen whether there is any trend/Seasonality.
Since the data has only trend applied holts exponential smoothing under forecast package. Sales of 2016 has been found and plotted.
Related
i have a WEEKLY dataset that start on 1986.01.03 and end on 2022-10-07.
The problem is when I forecast the time series with Arima +garch, because the date in T0 is wrong, i.e. 1975 enter image description here.
The function that I used to convert the dataset into time series is here, but I think that the problem is here, since it doesn't take on the right date.
FutureWeekly= ts(WeeklyFuture$FutureWeekly, start= c(1986,1), end = c(2022,10), frequency = 52)
does anyone know how to convert a weekly dataset to time series other than this?
There are the first rows of my dataset and then I have to transform that into returns (diff(log(FutureWeekly) to do the ARMA+GARCH
enter image description here
Try this:
futures<-c(WeeklyFuture$FutureWeekly) #convert to vector
FutureWeekly= ts(futures, start= c(1986,1,10), end = c(1986,3,7), frequency = 52) #add day of week ending on
One of the things ts() demands is a vector of values. I think it might also be easier for ts() to convert the data if it was able to see the 7-day increments.
Assuming you have full un-broken weekly data for the entire period, I think these two things will solve the problem.
In Stata I have a variable yearmonth which is formatted as 201201, 201202 etc. for the years 2012 - 2019, monthly with no gaps. When I format the variable as
format yearmonth %tm
The results look like: 2.0e+05 for all periods, with the exact same number each time. A Dickey-Fuller test tells me I have gaps in my data (I don't) and a tsfill command generates dozens of empty observations between each period.
How do I properly format my yearmonth variable so I can set it as a monthly date?
You do have gaps — between 201212 and 201301, for example. Consider a statement like
gen wanted = ym(floor(yearmonth/100), mod(yearmonth, 100))
which parses your integers like 201201 into year and month components. So floor(201201/100) is floor(2012.01) and so 2012 while mod(201201, 100) is 1. The two components are then the arguments of ym() which expects a year and a month argument.
Then and only then will your format statement do you want. That command won’t create date variables.
See help datetime in Stata for more information and Problem with displaying reformatted string into a four-digit year in Stata 17 for an explanation of the difference between a date value and a date display format.
I want to convert monthly data into quarterly averages. These are my 2 datasets:
gas <- UKgas
dd <- UKDriverDeaths
I was able to accomplish (I think) for the dd data as so:
dd.zoo <- zoo(dd)
ddq <- aggregate(dd.zoo, as.yearqtr, mean)
However I cannot figure out how to do this with the gas data...any help?
Follow-up
When I try to subset the data based on date (1969-1984) the resulting data does not include 1969 Q1 and instead includes 1985 Q1...any suggestions on how to fix this? I was just trying to subset as gas[1969:1984].
Originally I did not plan to post answer, as it looks like you did not pre-check your UKgas dataset to see that it is already a quarterly time series.
But the follow-up question is worth answering. "ts" object comes with many handy generic functions. We can use window to easily subset a time series. To extract the section between first quarter of 1969 and the final quarter of 1984, we can use
window(UKgas, start = c(1969,1), end = c(1984,4))
The result will still be a quarterly time series.
On the other hand, if we use "[" for subsetting, we lose object class:
class(UKgas[1:12])
#[1] "numeric"
I have a data frame with the following column names.
"week" "demand" "product-id"
The problem is to convert it into a time series object.
week is a number like 3,4,5,6,7,8,9 etc., and demand is in units and product-id is unique.
I want to convert the week column into time series, so as to prepare for modeling.
I want to predict weeks 10 and 11 demand by using an ARIMA model. How do I do that?
myTS <- ts(mydataframe[-1], frequency = 52)
will convert your demand and productId to a timeseries of 52 observations per year. For more elaborate timeseries, check package xts. Also compare this post on weekly data with ts.
Hi I am new to R and this time series forecasting.
I have a sample data of sales by day for past 3 years and I would like to use this data set to produce plot to find seasonality and pattern.
My daily data format is like eg..
Date, Sales
2010-01-01, 5
2010-01-03, 3
2010-01-04, 2
..
2011-12-01, 4
..
2014-11-01, 1
What I want to see is similar to below plot but by week and year using ts function. Also, due to leap year some year has 53 weeks and some 52 weeks, any idea how this taken into account when plotting ?
Playing with this ts function is not easy to me so it will be great if someone could help with this ..
You should start by creating a ts object. Check ?ts for the syntax, but assuming your data above were stored in `data', it's basically
tsData <- ts(data, start=c(2010,1), frequency=365)
where start refers to the (year, month) and frequency is the number of samples per year. Then you can use plot.ts() to plot the entire time series
plot.ts(tsData)
To extract seasonal patterns or trends, you can use the decompose() function.
decompose(tsData)
Here is an sample
x <- 1:10
y <- 11:20
plot(x, y)
lines(x,y)
On your data sales and date.
You can replace y with date and x with sales. If you still have issue, plz post it to me.