Concept library
1134 concepts across 20 domains and 101 tracks. Each track is a coherent sequence — read it top to bottom or dip in wherever the gap is.
All domains
01Foundations
02Transformer Internals
03Training & Fine-Tuning
04Reinforcement Learning
05Inference, Systems & Hardware
06Applied LLM Engineering
07Reasoning, Evaluation & Safety
08Multimodal & Applications
09Classical ML & Statistical Learning
10Causal Inference & Experimentation
11Time Series & Forecasting
12Graphs, Recommenders & Structured Data
13Generative Modelling Beyond Transformers
14Efficiency, Compression & Edge AI
15Search & Information Retrieval
16Data & Feature Engineering
17MLOps & Platform Engineering
18Security, Privacy & Adversarial ML
19Governance, Risk & Responsible AI
20Human-AI Interaction, Product & Economics
11
Time Series & Forecasting
Data with an arrow of time, where shuffling the rows destroys the problem.
4tracks
32concepts
377cards
3.8hreading
Time Series Foundations Stationarity, autocorrelation, ARIMA and state space models, seasonality and honest backtesting. 7 concepts · 133 cards
- 01 Seasonality and Decomposition Splitting a series into trend, seasonal and remainder components, and the choice between additive and multiplicative structure that determines whether the seasonal pattern grows with the level.
- 02 Autocorrelation and ARIMA Reading the autocorrelation and partial autocorrelation functions to identify how much of a series is explained by its own past, and what the AR, I and MA components each represent.
- 03 Backtesting and Temporal Validation Random cross-validation on time series lets a model learn from the future, and the alternatives all trade honesty against how much of the data can be used.
- 04 Exponential Smoothing and the ETS State-Space Form How a one-line recursive average became a family of likelihood-based models with prediction intervals and automatic selection, and why that family remains the benchmark sophisticated forecasters most often fail to beat.
- 05 Stationarity and Differencing Almost every time series method assumes the statistical properties do not change over time, and the transformations that enforce that assumption also change what the model is predicting.
- 06 Vector Autoregression and Granger Causality How a VAR models several series as jointly driven by their own pasts, what a Granger causality test actually establishes, and the specific ways predictive precedence differs from causation.
- 07 The Kalman Filter and Linear Gaussian State-Space Models How two alternating steps, predict and update, compute the exact posterior of a hidden state in a linear Gaussian model, why the same machinery yields the likelihood and a smoother, and what breaks when the model is wrong.
Forecasting at Scale Hierarchical reconciliation, probabilistic forecasts, global models and time-series foundation models. 12 concepts · 105 cards
- 01 Direct versus Recursive Multi-Horizon Forecasting The two ways to turn a one-step model into a 28-step forecast, why theory prefers direct estimation and practice keeps preferring recursion, and the hybrid strategies that sit between them.
- 02 Exogenous Covariates and Known-Future Inputs The three-way split between static attributes, covariates known into the future and covariates observed only up to now, why conflating them creates a silent look-ahead bug, and what each one is worth.
- 03 Forecast Combination and the Diversity Benefit Why averaging forecasts has beaten selecting the best one for fifty years, the variance algebra that explains it, and the combination puzzle where estimated optimal weights lose to the simple mean.
- 04 Global Models Versus Local Models Why fitting one model across thousands of series beats fitting one model per series, what cross-learning provides that per-series estimation cannot, and where local models still win.
- 05 Gradient-Boosted Trees for Forecasting How reframing a forecast as tabular regression over lag and calendar features made LightGBM the default winner of large-scale forecasting competitions, and the three things that framing cannot do.
- 06 Intermittent Demand Forecasting Why series that are mostly zeros defeat ordinary smoothing and ordinary error metrics, how Croston, SBA and TSB separate demand size from demand occurrence, and what the M5 competition showed about where machine learning helps and where it does not.
- 07 Cold-Start Forecasting for New Series What to predict for a series with no history, why attribute-based pooling and pretrained priors are two answers to the same question, and how to evaluate a regime you cannot backtest in the usual way.
- 08 Deep Forecasting Architectures: DeepAR, N-BEATS and TFT Three neural forecasting designs that each made a different bet (autoregressive likelihoods, deep residual stacks, and attention over typed covariates), what each is good for, and the evidence that simple baselines still beat many deep models.
- 09 Forecast Evaluation at Scale Why MAPE fails on the series that matter most, which scale-free metrics work instead, and how aggregating errors across a heterogeneous population hides the failures worth finding.
- 10 Hierarchical Forecasting and Reconciliation Why independently forecasting every level of a hierarchy produces numbers that do not add up, what reconciliation does about it, and why the optimal method also improves accuracy.
- 11 Probabilistic Forecasts and Quantile Loss Why a point forecast is insufficient for any decision involving asymmetric costs, how pinball loss trains quantiles directly, and what makes a distributional forecast well calibrated.
- 12 Time Series Foundation Models What a pretrained forecasting model transfers, why zero-shot forecasting is plausible at all, and how to evaluate the claim against a well-tuned classical baseline.
Anomaly & Changepoint Detection Residual-based detection, CUSUM, seasonal-hybrid methods, and the base-rate problem in alerting. 7 concepts · 72 cards
- 01 The Base Rate Problem in Alerting Why a detector with excellent sensitivity and specificity still produces mostly false alarms, the arithmetic that shows it, and the design responses that make an alerting system usable.
- 02 Bayesian Online Changepoint Detection How tracking a posterior over the time since the last change turns changepoint detection into exact, recursive Bayesian inference, what the run-length recursion computes at each step, and where its assumptions give way.
- 03 CUSUM and Sequential Change Detection Why detecting a small persistent shift needs accumulated evidence rather than a threshold, how the cumulative sum statistic works, and the tradeoff between detection delay and false alarm rate.
- 04 Evaluating Anomaly Detectors Why precision and recall computed per timestamp mislead on ranges, the point-adjustment protocol that inflated a decade of results, and what an honest evaluation reports.
- 05 Multivariate Time Series Anomaly Detection Why anomalies in systems of many sensors often live in broken relationships rather than in any single channel, the classical and deep detectors built to find them, and why the benchmark evidence for the deep ones is much weaker than their reported scores suggest.
- 06 Seasonal-Hybrid Decomposition for Anomalies Why anomaly detection on a seasonal series must remove the seasonality first, how robust decomposition avoids letting anomalies distort the fit, and the residual tests that follow.
- 07 Unsupervised Anomaly Detection Methods The three families of detector that need no labels, what notion of "anomalous" each encodes, and why the choice is a modelling assumption rather than a performance question.
Online & Streaming Learning Incremental updates, concept drift, regret bounds, and models that must learn from a moving distribution. 6 concepts · 67 cards
- 01 Concept Drift and Adaptive Windows How a streaming learner decides which past data still applies, why a fixed window is wrong in both directions, and the detectors that size the window from the data.
- 02 Incremental Model Updates in Production What it takes to update a deployed model continuously, why the appeal is usually smaller than it appears, and the failure modes that make scheduled retraining the safer default.
- 03 Online Gradient Descent and FTRL-Proximal How projected online gradient descent earns its square-root regret, why follow-the-regularised-leader is the same algorithm written lazily, and how that rewrite let Google train sparse click-through models on billions of features.
- 04 Online Learning and Regret Bounds What it means to learn without assuming a data distribution, why regret against the best fixed predictor is the achievable guarantee, and what online convex optimisation delivers.
- 05 Prequential Evaluation Why a stream has no test set, how test-then-train uses every example twice without leaking, and the forgetting mechanisms that make the resulting accuracy curve informative.
- 06 Streaming Algorithms and Sketches How to compute statistics over a stream too large to store, the three canonical sketches and their guarantees, and the mergeability property that makes them work in distributed systems.