Forecasting levels is easier. Forecasting extremes is almost always harder. Highs and lows are properties of the path, not just the close. Price must actually reach the level, not merely close beyond it. Most models break on that distinction. 1) The data behave like noise Bitcoin is non‑stationary. Volatility shifts. Tails are fat. Jumps are normal. Terminal models compress everything to one number. Extremes require reasoning about the entire path. ...
Forecasting where there is almost no structure Most financial time series are noise. No trend. No seasonality. Weak repeatability. The best baseline is a random walk. Any discovered pattern disappears as soon as it becomes part of the market. What still works Structure is scarce — but signal can be layered. Statistics captures basic forms. ML captures weak dependencies. AI captures hidden links in text and context. Crowd forecasting captures human information. ...
My first forecast was simple: BTC would not drop below $90,000 in November. The bet looked reasonable from the data. But it lost. Here’s why. 1) I overestimated regime stability The month’s range looked stable: ~95–110k. Std was low; swings looked “orderly.” Mistake: I treated a local structure as durable. Regimes can switch faster than the data reveals. 2) I underestimated out‑of‑range probability The drop happened without a single clear trigger. That’s normal: price can break the distribution on order‑flow or liquidity alone. ...
Forecasting works where the data have structure. Stocks have weak structure. ⸻ Weak patterns Trends break on news. Seasonality is minimal. Cycles are unstable. Autocorrelations are low. The data behave like noise. ⸻ The main drivers are outside the time series Future prices depend on what is not in the history: regulator decisions, earnings, liquidity, events, market sentiment. The time series does not contain causes. ⸻ Predictors are unknown or unpredictable It’s not enough to know the variables — you must forecast them. With stocks, that’s hard or impossible. ...
In forecasting, the key is not the method but the fit between data and the task. Every forecast depends on which data we use and what remains after removing noise. There are three levels. Raw data. Observations, prices, news. Without interpretation — just a stream. Processing. Smoothing, filters, feature selection. An attempt to preserve structure and remove the rest. Models. From simple heuristics to statistics and ML. The model should be simple enough to work, and complex enough to reflect probability. ...
Any forecast consists of five steps. It sounds simple. In practice — it’s discipline. 1. Problem definition. Not “what to predict?”, but how it will be used. For prediction markets: which event, what horizon, what success criterion. 2. Information gathering. Two sources: data and context. Sometimes the data are scarce — then understanding the mechanism matters more. 3. Preliminary analysis. Plots, trends, jumps, seasonality, outliers. You need to see the structure before you build a model. ...
In forecasting, there are three types of models. Models with predictors. They use external variables — temperature, the economy, human behavior. These models explain why something happens. Time‑series models. They look only at the past values of the series. They explain nothing, but often predict better. Hybrid models. A combination of past dynamics and external factors. Why is it sometimes better to use only a time series? Because external variables may be unknown, hard to measure, or unpredictable themselves. Sometimes the goal is simply to predict, not to explain. ...
On prediction markets the key is to judge how predictable the event itself is. There are four conditions that determine this. Understanding the drivers. If the structure of the event is clear, you can assign a probability. If the factors are fuzzy, forecasting turns into guessing. Availability of data. The more data and repeatable patterns, the better. If data is scarce, the market becomes a psychological game. Similarity of the future to the past. Typical events are predictable. Unique events are almost not. ...
Polymarket is a prediction market. The stakes here are not about gambling, but about probabilities. Each market is a question about the future: elections, the economy, technology, sports, or world events. The price of a contract reflects the crowd’s probability estimate of the outcome. If a contract trades at 0.23, the market implies about a 23% chance the event will occur. The core idea is simple: when people put money on the line, they pay closer attention to information. That’s why these markets often react faster than analysts and media. ...
I’m starting to study forecasting to think more precisely. Most forecasts are confident words without probabilities. I care less about asserting and more about testing. Here I will log the learning process: how my estimates change, what turns out to be noise, and how the model is corrected. First steps are simple: observe, assign probabilities, remove unnecessary assumptions. The model will change — and that’s part of the process. This blog is not about being right. It’s about honesty under uncertainty. ...