Daily to Weekly and Daily to Monthly Aggregation

Salutation for the day.

Thank you for the fantastic package design.

I was unsure if this package could aggregate time series from daily to weekly and daily to monthly because when I attempt to do so, I receive an error saying there isn't an option for "weekly" or "monthly".

I have put a reproducible example for this.

` library(Sim.DiffProc) library(stats) library(tempdisagg) set.seed(1234)

dates <- seq(as.Date("2019-01-01"), as.Date("2023-12-31"), by = "1 day")

X <- GBM(N = length(dates)-1, M = 1, x0 = 50, y= 200) daily_data <- data.frame(dates,X) daily_data <- ts(daily_data$X1, start = c(2019,1,1), end = c(2022,12,31), frequency = 365) weekly_data <- ta(daily_data, conversion = "sum", to = "weekly") monthly_data <- ta(daily_data, conversion = "sum", to = "monthly") ` Error in Console: daily_to_weekly_and_monthly_error

I'm not sure how to get this working as it is needed for the use case I'm working on. Any help on this will be appreciated.

Thanks in advance.

The main point of tempdisagg, is to disaggregate:

The classic methods allow you to convert annual or quarterly series into regular quarterly or monthly series. See here.
tempdisagg now allows disaggregating into daily series. See the last example in ?td.
With some trickery, you can do quarterly to weekly in a way that takes into account that different overlap between weeks and quarters in different years. See here
Finally, you can convert to hourly data. See here

What you are up to it to aggregate, which is a far easier task. tsbox can solve the problem you are struggling with:

library(dplyr)
library(tsbox)

dates = seq(as.Date("2019-01-01"), as.Date("2023-12-31"), by = "1 day")
daily <- tibble(
  time = dates,
  value = rnorm(length(dates))
) 

daily
#> # A tibble: 1,826 × 2
#>    time         value
#>    <date>       <dbl>
#>  1 2019-01-01  0.350 
#>  2 2019-01-02 -1.12  
#>  3 2019-01-03  0.384 
#>  4 2019-01-04 -0.0618
#>  5 2019-01-05  0.764 
#>  6 2019-01-06  1.09  
#>  7 2019-01-07  0.660 
#>  8 2019-01-08 -0.411 
#>  9 2019-01-09  0.893 
#> 10 2019-01-10  0.0300
#> # … with 1,816 more rows

tsbox::ts_frequency(daily, to = "month")
#> # A tibble: 60 × 2
#>    time          value
#>    <date>        <dbl>
#>  1 2019-01-01  0.111  
#>  2 2019-02-01  0.183  
#>  3 2019-03-01 -0.108  
#>  4 2019-04-01 -0.103  
#>  5 2019-05-01  0.00898
#>  6 2019-06-01  0.111  
#>  7 2019-07-01 -0.141  
#>  8 2019-08-01  0.213  
#>  9 2019-09-01 -0.100  
#> 10 2019-10-01  0.134  
#> # … with 50 more rows
tsbox::ts_frequency(daily, to = "year")
#> # A tibble: 5 × 2
#>   time          value
#>   <date>        <dbl>
#> 1 2019-01-01  0.0375 
#> 2 2020-01-01  0.00706
#> 3 2021-01-01  0.0208 
#> 4 2022-01-01 -0.00932
#> 5 2023-01-01  0.0706
tsbox::ts_frequency(daily, to = "week")
#> # A tibble: 260 × 2
#>    time         value
#>    <date>       <dbl>
#>  1 2019-01-06  0.342 
#>  2 2019-01-13  0.313 
#>  3 2019-01-20 -0.226 
#>  4 2019-01-27 -0.108 
#>  5 2019-02-03 -0.292 
#>  6 2019-02-10  0.292 
#>  7 2019-02-17  0.658 
#>  8 2019-02-24 -0.392 
#>  9 2019-03-03  0.0588
#> 10 2019-03-10 -0.581 
#> # … with 250 more rows

And if you need seem instead of averages:

tsbox::ts_frequency(daily, to = "week", aggregate = "sum")
#> # A tibble: 260 × 2
#>    time        value
#>    <date>      <dbl>
#>  1 2019-01-06  2.40 
#>  2 2019-01-13  2.19 
#>  3 2019-01-20 -1.59 
#>  4 2019-01-27 -0.759
#>  5 2019-02-03 -2.05 
#>  6 2019-02-10  2.04 
#>  7 2019-02-17  4.61 
#>  8 2019-02-24 -2.74 
#>  9 2019-03-03  0.412
#> 10 2019-03-10 -4.06 
#> # … with 250 more rows

^{Created on 2023-04-20 with reprex v2.0.2}

christophsax / tempdisagg

Daily to Weekly and Daily to Monthly Aggregation #51