## Feature #656

### Improved model specification for trend1d, trend2d, grdtrend

Status: | In Progress | Start date: | 2014-12-10 | |
---|---|---|---|---|

Priority: | Normal | Due date: | ||

Assignee: | Paul | % Done: | 0% | |

Category: | - | |||

Target version: | Candidate for next minor release | |||

Platform: |

**Description**

These modules fit trends to 1-D, 2-D, and grids. However, they are limited in what models to fit. For instance, we have received requests to allow trend1d to fit a seasonal signal plus trend but we cannot currently do this. This issue tries to define how the models should be specified to allow for more flexibility.

**trend1d**: Currently takes -N[f]<n>[r], for polynomial of *n* terms, prepend **f** for Fourier series instead; append **r** for robust.

Suggestion: -N[f|s|c]<list-of-terms>[,[f|s|c]<list-of-terms>,...][r]

Here, *list-of-terms* is such that we include all x^n, for all *n* in the *list-of-terms*. E.g., -N0,2 would fit m_0 + m_2 * x^2, while -N0-3 would fit m_0 + m_1*x + m_2 * x^2 + m_3 * x^3. If *list-of-terms* starts with **f** then we fit the Fourier terms c_n * cos (2*pi*n*x/T) + s_n * sin (2*pi*n*x/T), where *T* is the length of the input series. If **c** or **s** is used instead of **f** then we only add the cosine or sine term. A trailing **r** means robust fit.

FInal example: -N0-1,f1 would fit model y(x) = m_0 + m_1 * x + c_1 * cos (2*pi*x/T) + s_1 * sin (2*pi*x/T). Note: Either 0 or f0 can be used to include the constant m_0.

**trend2 and grdtrend**: Currently takes -N<n_model>[r], where *n_model* is in the 1-10 range (10 means full bicubic model). There is no **f** modifier.

I suggest trend2d and grdtrend should have the same syntax as trend1d, with the understanding that an *n* like 3 would mean "include x^3, x^2y y^2x, and y^3". This means we would not be able to tinker with models that exclude some of these terms (which we cannot do in the current version anyway), Here, **f,c,s** would include Fourier terms in both x and y.

Example: -N0-2,f1 would fit z(x,y) =m_0 + m_1*x + m_2*y + m_3*x^2 + m_4*x*y + m_5*y^2 + cx_1 * cos (2*pi*x/X) + sx_1 * sin (2*pi*x/X) + cy_1 * cos (2*pi*y/Y) + sy_1 * sin (2*pi*y/Y)

where*X*and

*Y*are the length of the x and y dimensions. This proposal has several benefits:

- Dramatically generalizes these modules and allow for needed flexibility.
- It also extends the Fourier building blocks to 2-D.
- Keeps the syntax consistent across three modules
- Compact notation for the 2-D cases

If anyone wants the 2-D fits to be able to exclude cross-terms etc then one could consider -N[x|y||xy][f|s|c]<list-of-terms>[,[f|s|c]<list-of-terms>,...][r] but this would be messier.

### Associated revisions

Lay groundwork for parsing of -N for new trend models; see issue #656

### History

#### #1 Updated by Paul over 2 years ago

- Require
**p**for polynomial instead of it being a default basis. - Use
**+r**rather than just**r**to indicate robust fit. - Accept
**+x**<period> (and**+y**<period> for trend2d and grdtrend) to set the fundamental period(s) [data domain].

#### #2 Updated by Paul over 2 years ago

- -Np2 would mean polynomial of order two and if 2-D would include x^2, y^2 and xy, i.e., the scheme above.
- -Nxx,yy would only select x^2 and y^2 but not x*y. We would accept things like xxxyy or x3y2. When x and y are used the p is not required.

#### #3 Updated by Paul almost 2 years ago

**Status**changed from*New*to*In Progress***Assignee**set to*Paul*

The 1-D method outlined above for trend1d was implemented in 5.2.1. Work remains to explore the 2-D portion to make sure it is not overly complex.