Time Series Decomposition of weekly data

r, statistics, time-series

Solution

Welcome to R!

Yes, the frequency is 52.

If the data is not already classed as time-series, you will need both `ts()` and `decompose()`. To find the class of the dataset, use `class(data)`. And if it returns `"ts"`, your data is already a time-series as far as R is concerned. If it returns something else, like `"data.frame"`, then you will need to change it to time-series. Assign a variable to `ts(data)` and check the class again to make sure.

There is a monthly time-series dataset `sunspot.month` already loaded into R that you can practice on. Here's an example. You can also read the help file for `decompose` by writing `?decompose`

class(sunspot.month)
[1] "ts"

> decomp <- decompose(sunspot.month)

> summary(decomp)

         Length Class  Mode     
x        2988   ts     numeric  
seasonal 2988   ts     numeric  
trend    2988   ts     numeric  
random   2988   ts     numeric  
figure     12   -none- numeric  
type        1   -none- character

> names(decomp)
[1] "x"        "seasonal" "trend"    "random"   "figure"   "type"    

> plot(decomp)  # to see the plot of the decomposed time-series

The call to `names` indicates that you can also access the individual component data. This can be done with the `$` operator. For example, if you want to look at the seasonal component only, use `decomp$seasonal`.

r time-series

Problem

I am totally new to R and have just started using it. I have three years of weekly data. I want to decompose this time series data into trend, seasonal and other components. I have following doubts: - Which function I should use - `ts()`or `decompose()` - How to deal with leap year situation. Please correct me if I am wrong, the frequency is 52. Thanks in Advance. I would really appreciate any kind of help.

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