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01 ·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. 02 ·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.