Reconciliation interface design

User-defined control parameters.

1. Construction

• Projection
• Structural
• ERM (low-priority)

2. Weight matrix (typically requires access to model object and varies with data structure)

• OLS
• WLS
• Structural
• Sample
• Shrinkage
• More common types...
• Time varying (maybe?)
• Custom matrix

3. Optimisation technique

• Regular minimisation
• Non-negative (LP, Heuristic)
• Constraint matrix LP

4. Data structure

• Cross-sectional (Hierarchical & Grouped)
• Temporal (Hierarchical & Grouped)
• Cross-temporal
• Arbitrary acyclical graphs (maybe?)
• Disjoint

5. Combination method/type

• Additive
• Linear combination

Are there more things that can be customised here?

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User interface

Data structure and value combination method/type

Data structure and combination method are passed in via data attributes created at the aggregate_*() step.
Allow the user to directly impose data structure constraints, for example defining a pre-existing aggregation structure from the data.
This can also be used to remove aggregation structure to create disjoint hierarchies
For example, you may have a cross-temporal structure but only want to make it temporally coherent. To achieve this, you can remove the key aggregation constraints.

Hold onto aggregation structure in <tsibble>, and <mdl_lst>

Code

Allow reconciliation of mables, fitted models, and model definitions.

Option A - reconcile() on model with all params as args

reconcile(<mbl_df>, lm = min_trace(lm, ...), ...) # as before, maybe soft-deprecated?

mutate(<mbl_df>,  lm = reconcile(lm, ...), ...)
mutate(<mbl_df>,  lm_ols = reconcile(lm,weights = weight_ols), lm_shr = reconcile(lm,weights = weight_shr), ...)

reconcile(<mdl_lst>, ???) 
reconcile(<mdl_def>, ???)

reconcile(object, weights = weight_fn, construction = constr_fn, opt_method = opt_fn)

Option B - reconcile() on mable with opt function as reconcile input fn

reconcile(<mbl_df>, lm = gls(lm, weights = weight_fn,, ...), ...)
reconcile(<mbl_df>, lm = nn(lm, weights = weight_fn, ...), ...)
reconcile(<mbl_df>, lm = lp_constrained(lm, weights = weight_fn,, ...), ...)

reconcile(<mdl_lst>, opt_fn = gls, weights = weight_fn, ... ) #??? 
reconcile(<mdl_def>, ???)

Option C - reconcile() on mable with construction function as reconcile input fn

reconcile(<mbl_df>, lm = proj(lm, weights = weight_fn, ...), ...)
reconcile(<mbl_df>, lm = struc(lm, weights = weight_fn, ...), ...)

reconcile(<mdl_lst>, opt_fn = gls, weights = weight_fn, ... ) #??? 

Option D - reconcile() on mable with node utilisation function as reconcile input fn

reconcile(<mbl_df>, lm = top_down(lm, weights = weight_fn, optimiser = opt_fn, ...), ...)
reconcile(<mbl_df>, lm = middle_out(lm, weights = weight_fn, optimiser = opt_fn, ...), ...)
reconcile(<mbl_df>, lm = bottom_up(lm, weights = weight_fn, optimiser = opt_fn, ...), ...)
reconcile(<mbl_df>, lm = all_nodes(lm, weights = weight_fn, optimiser = opt_fn, ...), ...)

Attention: @danigiro, @robjhyndman, @GeorgeAthana