#timeseries

4 articles

01 ·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. 02 ·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. 03 ·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. 04 ·Time Series·FREE·11 min read RNNs and LSTMs from Scratch — Why Learn Them in the Transformer Era Start from one idea — read a sequence one step at a time while carrying a state — then work out why multiplying the same matrix over and over kills the gradient, and what the three LSTM gates actually fixed. Ends with why Transformers took over, and where this recurrent idea still wins.