My Expert Forecasts Got Crushed by a Dumb Algorithm
Note: This post is initially composed with deep research and finalized by me.
I have to admit something humbling. After spending months building what I thought were good forecasting models (MAPE less than 15%), I ran a check against the simplest possible approach: a naive forecast (moving average). The result? My expert judgment, backed by complex models and deep domain knowledge, was consistently less accurate. 🤦‍♂️
This wasn’t just a fluke. It was a classic “expert paradox,” an experience that forced me to re-evaluate how we approach forecasting. It turns out that our greatest asset—our expert brain—can also be our biggest liability, thanks to a host of cognitive biases. This post is my deep dive into why simple baselines are so powerful and how I’m changing my approach to forecasting for good.
The Underrated Power of Simple Baselines
Before we get into why my expert judgment failed, let’s talk about the simple, “dumb” methods that beat me. These are essential baselines that should be the starting point for any forecasting project. They are the ultimate test of whether your complex model is actually adding any value.
- Random Walk Forecast: This is the simplest of all. It assumes the forecast for the future is just the last observed value. It’s surprisingly effective for many financial and economic time series.
- The logic: “Tomorrow will be the same as today.”
- The math: \(\hat{y}_{T+h|T} = y_T\) (The forecast \(\hat{y}\) for a future time \(T+h\) is simply the value at the last observation \(y_T\).)
- Seasonal Naive Method: This is for data with clear seasonal patterns. The forecast is simply the value from the same season last year.
- The logic: “This April will be like last April.”
- The math: \(\hat{y}_{T+h|T} = y_{T+h-m}\) (The forecast is the value from one seasonal period \(m\) ago.)
- Average Method: Here, the forecast for all future periods is just the mean of all your historical data. It’s stable but doesn’t adapt to recent changes.
- The logic: “The future will be the average of the past.”
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The math: $$\hat{y}_{T+h T} = \bar{y} = \frac{1}{T}\sum_{t=1}^{T}y_t$$
These methods are powerful because they are objective, transparent, and robust against overfitting. They don’t have egos or bad days. They mechanically execute a simple rule, which protects them from the very thing that tripped me up: cognitive bias.
Why My Brain Failed Me: The Peril of Cognitive Biases
So, why did my judgment perform so poorly? Because even with deep expertise, the human mind takes mental shortcuts (heuristics) that lead to systematic errors. A mechanical approach like the naive method is immune to these. Looking back, I could see these biases clearly in my own thinking.
| Cognitive Bias | My Experience in a Nutshell | Impact on My Forecast |
|---|---|---|
| Anchoring Bias | I was stuck on an initial piece of information, like last quarter’s strong performance, and didn’t adjust enough for new data. | My forecasts were too slow to react to changing market conditions. |
| Overconfidence Bias | I was excessively confident in my own judgment, believing I could “outsmart” the market’s randomness. This made me downplay uncertainty. | My prediction intervals were way too narrow, and I was shocked by “unexpected” outcomes. |
| Confirmation Bias | I subconsciously looked for news articles and data points that confirmed my initial hypothesis, while ignoring contradictory evidence. | I created an echo chamber that reinforced my flawed assumptions. |
| Availability Heuristic | A recent, dramatic market event was fresh in my mind, causing me to overweight its importance for future predictions. | My forecasts overreacted to salient but not necessarily representative information. |
| Optimism / Wishful Thinking | Because I was invested in a project’s success, I was hesitant to forecast a negative outcome. My hopes clouded my realistic estimates. | My forecasts were consistently too optimistic, driven by what I wanted to happen. |
These biases aren’t a sign of incompetence; they are a fundamental part of human cognition. My mistake wasn’t having them; it was failing to have a system in place to counteract them. The simple naive forecast won because it was, by its very nature, unbiased.
My New Forecasting Playbook: A Hybrid Approach
This humbling experience led me to adopt a new, more rigorous forecasting framework. It’s all about leveraging the strengths of both statistical methods and expert judgment while mitigating their weaknesses.
1. Baselines Are Mandatory
No complex model gets a pass without first proving it can consistently beat a simple baseline (like naive or seasonal naive). If your fancy ML model can’t outperform “tomorrow will be like today,” you have a problem. This is my non-negotiable first step.
2. Systematically Mitigate Bias
I can’t eliminate my cognitive biases, but I can tame them. My new process involves:
- Writing down my assumptions before making a forecast.
- Seeking out disconfirming evidence on purpose.
- Separating the forecast from the target. A forecast is a realistic estimate, not a goal. These two things must be kept separate to avoid wishful thinking.
3. Combine, Don’t Just Choose
The best approach is often a hybrid one. I now use a simple statistical forecast as my objective anchor. Then, I use my expert judgment to make structured adjustments for things the model can’t possibly know—a new product launch, a planned marketing campaign, or a looming supply chain disruption. This combines the objectivity of the machine with the contextual intelligence of the human.
4. Prioritize Interpretability
A “black box” forecast that no one understands is a forecast no one trusts or acts on. Simple models are transparent by nature. For more complex models, I now put extra effort into explaining the “why” behind the numbers. An accurate forecast is useless if it’s not actionable.
Final Thoughts
Getting beaten by a simple algorithm was a crucial lesson in intellectual humility. It taught me that in the world of forecasting, complexity is not a virtue in itself. Objectivity and consistency often trump subjective expertise.
My advice now is simple: respect the power of simplicity. Start with a naive baseline, be ruthlessly honest about your own cognitive biases, and build a process that combines the best of both worlds—statistical rigor and expert insight. Your accuracy will thank you for it.
