#forecasting

12 articles

01 ·Time Series·★ MEMBER·PAPER·11 min read Paper Explained: Holi-World — Turning First-Person Recordings into a Ledger of the Room, Then Teaching a VLM to Reason over It Holi-World (arXiv:2610.26549) automatically turns first-person recordings into a record of room geometry, furniture, and the trajectories of objects people pick up, then converts that record into about 80K spatial and temporal QA pairs to train a 4B vision-language model. A from-scratch walkthrough of the pipeline, the numbers, and the limits. 02 ·Time Series·★ MEMBER·PAPER·7 min read Paper Explained: STEPQuant — When and Where Quantization Errors Matter in Delta-Rule Recurrent States Linear-attention states have a fixed size, yet under concurrent serving they can outgrow the model weights. Naive low-bit quantization lets errors pile up with every update. STEPQuant spends bits and scales according to how long an error survives and which row it lands on. Explained from scratch. 03 ·Time Series·★ MEMBER·PAPER·8 min read Paper Walkthrough: PointWAM — Forecasting the Scene and the Hands as 3D Point Trajectories to Teach Robot Hands from Human Video PointWAM splits the world into scene points and hand keypoints, forecasts where both will move in one shared 3D frame, then converts the hand forecast into robot actions. Pre-trained on 1.15M human-video episodes, it lifts average DexJoCo success from 12.1% to 69.0%. A from-scratch walkthrough grounded in the paper. 04 ·Time Series·★ MEMBER·PAPER·7 min read Paper Explained: Video Generation Models: A Survey of Post-Training and Alignment — Four Toolboxes for Making Video Models Do What You Ask A from-scratch reading of the survey that organizes how pretrained video generators are steered toward instruction-following, temporal coherence, physical plausibility, and safety without retraining—via supervised fine-tuning, self-training and distillation, preference and reward optimization, and inference-time methods. 05 ·Time Series·★ MEMBER·PAPER·13 min read Paper Review: MetroLLM-Bench — What Happens When an LLM Runs the Ticket Machine. Why a 2.6 GB 4B Model Tied GPT-5.6, and Why Fine-Tuning Hurt at 27B A 955-case benchmark that asks how well a language model can serve as the decision layer of a transit ticket kiosk. We read, from the paper alone, how a 4B QLoRA student beat GPT-5.6 on the deterministic tier and why PEFT gains shrink with model size until they turn negative at 27B. 06 ·Time Series·★ MEMBER·PAPER·11 min read Paper Explained: WearableQA — Testing Whether LLMs Can Reason Over 500 Days of a Real User's Wearable Data 4,084 ten-option questions built from 200 real users' daily wearable records (up to 500 days each) plus blood panels. Separating 'answer from knowledge' from 'read it off the numbers' splits 14 models between 19.6% and 72.9% accuracy, and simply giving the model Python adds 20 points. A ground-up walkthrough of how the questions are built and what the results actually say. 07 ·Time Series·★ MEMBER·PAPER·12 min read H3-World: Turning Language Understanding into World Control — Paper Explained H3-World turns 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. 08 ·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. 09 ·Time Series·FREE·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. 10 ·Time Series·FREE·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. 11 ·Time Series·FREE·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. 12 ·Time Series·FREE·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.