tempdisagg | Temporal Disaggregation and Interpolation of Time Series | Time Series Database library

by christophsax R Version: 0.24 License: No License

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kandi X-RAY | tempdisagg Summary

tempdisagg is a R library typically used in Database, Time Series Database applications. tempdisagg has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Temporal disaggregation methods are used to disaggregate or interpolate a low frequency time series to a higher frequency series, where either the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series. Temporal disaggregation can be performed with or without one or more high frequency indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso, Fernandez, Litterman, Denton and Denton-Cholette. Supports most R time series classes.

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tempdisagg has a low active ecosystem.

It has 30 star(s) with 4 fork(s). There are 7 watchers for this library.

It had no major release in the last 12 months.

There are 7 open issues and 27 have been closed. On average issues are closed in 283 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tempdisagg is 0.24

Quality

tempdisagg has 0 bugs and 0 code smells.

Security

tempdisagg has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tempdisagg code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tempdisagg does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

tempdisagg releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

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tempdisagg Examples and Code Snippets

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Community Discussions

Trending Discussions on tempdisagg

How can I use the `td` command from the `tempdisagg` package to disaggregate monthly data into daily data frequency?

Change frequency time series in R

QUESTION

How can I use the `td` command from the `tempdisagg` package to disaggregate monthly data into daily data frequency?

Asked 2020-Jan-03 at 00:25

I have a monthly frequency data which I am trying to disaggregate into a daily frequency data. So I use the td command from the tempdisagg package in R using the code below:

...

ANSWER

Answered 2020-Jan-03 at 00:25

It looks like the tempdisagg package doesn't allow for monthly to daily disaggregation. From the td() help file 'to' argument:

high-frequency destination frequency as a character string ("quarterly" or "monthly") or as a scalar (e.g. 2, 4, 7, 12). If the input series are ts objects, the argument is necessary if no indicator is given. If the input series are vectors, to must be a scalar indicating the frequency ratio.

Your error message "'to' argument: unknown character string" is because the to = argument only accepts 'quarterly' or 'monthly' as strings.

There is some discussion about disaggregating monthly data to daily on the stats stackexchage here: https://stats.stackexchange.com/questions/258810/disaggregate-monthly-forecasts-into-daily-data

After some searching, it looks like nobody consistently using disaggregated monthly to daily data. The tempdisagg package seems to be capable of what most others have found to be possible -- yearly to quarterly or monthly, and time periods that are consistent even multiples.

Eric, I've added a script below that should illustrate what you're trying to do, as I understand it.

Here we use real pricing data to move from daily prices -> monthly prices -> monthly returns -> average daily returns.

Source https://stackoverflow.com/questions/59417660

QUESTION

Change frequency time series in R

Asked 2018-Jan-15 at 16:09

As input I have a time series known at daily time points. We may refer to this time series as _x_. The object _x_ is a time series, i.e. consisting of dates and measurements.

I would like to investigate the effect of keeping the time series constant in each month. In other words, I would like to have a new time series _y_ that still has a daily frequency but the observations in the months are kept constant equal to the first value in the month.

I have looked into this and tried to use the 'tempdisagg' package, specifically the 'td' function. However, this seems too complicated for my purpose and requires a formula as input. Furthermore supposing each month has 30 days will create a discrepancy between _x_ and _y_, which is not desirable as the length of the time series is several years.

Is there a solution to enable this?

...

ANSWER

Answered 2018-Jan-15 at 16:09

Creation of x (time series)

Source https://stackoverflow.com/questions/48265772

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You can download it from GitHub.

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