effort prediction dev - "safety weights" - are they only being applied at the final estimation?! Is this right? #37
Description
- Looking at the code the 2025 weight (in recognition of the fact that it has error associated with it) isnt done until the very last step. This seems incorrect, but maybe its not. Seems that perhaps it should be used elsewhere but Gemini only implemented it here at the end.
- Secondary issue - Holt does not use weights. Here's why:
Me: Dont change code. I want to explore this question - it looks like the holt model does not accept weights, but our other models do, is this a problem for our analysis?
This is a sharp observation. You have identified a methodological inconsistency in Step 4.
The Short Answer: Yes, it is a "problem" in the sense that Holt creates a loophole in your Safety Brake. If the Holt model wins the tournament, your final 2026 forecast will ignore the "Ratio Weighting" you just designed and will treat the 2025 imputation as a hard fact (Weight = 1.0).
However, it is not necessarily a fatal flaw because of how Holt works mathematically.
Here is the breakdown of why this happens, the specific risk it creates, and why we generally accept it.
- Why Holt Ignores Weights (The "Internal" Weighting)
Your GLM and Robust models are "Static" regressions. They view all data points at once. To tell them "Observation 12 is less reliable than Observation 11," you must supply external weights.
Holt is different. It is a State Space model (ETS). It calculates its own weights dynamically using the Smoothing Parameter (α).
Concept: Holt calculates a weighted average where weights decay exponentially into the past.
The Conflict: If you try to force external weights on it, you fight against its internal logic. The algorithm's entire purpose is to determine how much to trust the most recent point (2025) versus the previous level (2024).
- The Specific Risk to Your Analysis
Because Holt bypasses your "Safety Brake," it is more sensitive to a bad Step 1 imputation than your Weighted GLM.
Scenario:
Step 1 Imputation predicts a massive spike for 2025 (e.g., 500 → 800 trips).
You know this is uncertain, so you set Weight = 0.5.
Weighted GLM: Sees the spike, but also sees the low weight. It splits the difference and predicts a moderate 2026 (e.g., 600).
Holt: Sees the spike. It ignores your weight. It thinks "The level has shifted up to 800." It projects that new level forward (e.g., 800+).
The Result: If Holt wins the leaderboard, your final forecast will be more volatile/responsive to the 2025 imputation than if GLM had won.
- The Counter-Argument (Why we keep it)
Even though it ignores the safety brake, Holt has two features that act as a "Natural Brake":
Damping: We are using damped=TRUE. This prevents the "Rocket Ship" effect. Even if Holt trusts the 2025 spike, it will assume the trend flattens out, rather than projecting exponential growth forever.
Selection Bias (The Good Kind): Remember Step 2 (Backtesting). If Holt was highly sensitive to outliers, it likely would have produced huge errors during the volatile test years (2023-2025). The fact that Holt won your leaderboard suggests that, for this specific stratum, the fishery is stable enough that Holt's internal smoothing is working correctly.
Verdict
It is an acceptable inconsistency.
You are trading "Consistency of Weighting" for "Diversity of Models."
If we removed Holt, you would only be testing regression-based models (GLM, GAM, Robust). You would lose the entire class of "Adaptive" models.
Given that fisheries often drift over time (which Holt handles beautifully), keeping Holt is worth the risk that it ignores your manual safety brake.
No code change is recommended. The comment I added to the code (# Holt ignores weights) is sufficient to document this behavior for future researchers.