#forecasting
6 articles
01
·Time Series·★ MEMBER·PAPER·12 min read
H3-World, Explained — Turning Language Understanding into World Control
How to turn a 33B video generator into a controllable world model without bolting on an action module: express actions as sentences, bind each sentence to a video-latent interval, and use attention routing to stop the crosstalk. Only 0.199% of parameters are trained.
02
·Time Series·★ MEMBER·PAPER·11 min read
Time-Series Anomaly Detection — The Math Behind the Alerts
An alert should fire on the gap between what you observed and what that moment predicted — not on the raw size of a number. Four rulers for measuring that gap (robust statistics, forecast residuals, subsequence distance, changepoints), built up from analogy to formula, then the two things that actually break in production: how you pick the threshold and how you evaluate.
03
·Time Series·★ MEMBER·PAPER·11 min read
Do Transformers Actually Work on Time Series? — The Argument and the Practical Answer
In 2022 a single linear layer beat the whole crop of time-series Transformers on the standard benchmarks. The culprit turned out to be tokenization, not attention — a diagnosis PatchTST fixed by patching and iTransformer by transposing the axes. And yet gradient boosting keeps winning the practitioner competitions, for reasons that come down to the shape of real data.
04
·Time Series·★ MEMBER·PAPER·11 min read
Do Transformers Actually Work on Time Series? — The Argument and the Practical Answer
In 2022 a single linear layer beat the whole crop of time-series Transformers on the standard benchmarks. The culprit turned out to be tokenization, not attention — a diagnosis PatchTST fixed by patching and iTransformer by transposing the axes. And yet gradient boosting keeps winning the practitioner competitions, for reasons that come down to the shape of real data.
05
·Time Series·★ MEMBER·9 min read
Time-Series Forecasting from Scratch — From Classical Methods to Foundation Models
What makes forecasting different from ordinary regression is that the rows are not independent and the test set is always in the future. Autocorrelation and stationarity first, then the intuition behind ARIMA, seasonal and trend decomposition, and where deep and foundation models actually sit. The last third is evaluation — no random splits, how to backtest, and why MAPE lies.
06
·Time Series·★ MEMBER·9 min read
Time-Series Forecasting from Scratch — From Classical Methods to Foundation Models
What makes forecasting different from ordinary regression is that the rows are not independent and the test set is always in the future. Autocorrelation and stationarity first, then the intuition behind ARIMA, seasonal and trend decomposition, and where deep and foundation models actually sit. The last third is evaluation — no random splits, how to backtest, and why MAPE lies.