> For the complete documentation index, see [llms.txt](https://docs.algenta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.algenta.ai/time-series-and-statistics.md).

# Time series & statistics

- [Forecasting and backtesting](https://docs.algenta.ai/time-series-and-statistics/forecasting.md): Forecast a series, measure the method out of sample, put an honest band around it, and make a hierarchy add up — with a refusal wherever the answer does not exist.
- [ES-RNN residual correction](https://docs.algenta.ai/time-series-and-statistics/ml-esrnn.md): ES-RNN-class residual correction over a frozen exponential-smoothing backbone — backbone selection, a per-series-adaptive window feature, and two correctors that consume it.
- [Time-series decomposition and queueing kernels](https://docs.algenta.ai/time-series-and-statistics/time-series-decomposition-and-queueing-kernels.md): Closed-form M/M/1, M/M/c, and Erlang B/C queueing formulas, plus additive time-series decomposition (trend, seasonal, residual) and a lightweight STL-like iterator -- two unrelated formula libraries s
- [Forecast scoring & Murphy decomposition](https://docs.algenta.ai/time-series-and-statistics/forecast-scoring.md): Score probabilistic forecasts honestly — Brier skill, calibration error, and the Murphy decomposition that separates being wrong from being overconfident.
- [Statistical inference](https://docs.algenta.ai/time-series-and-statistics/statistical-inference.md): p-values that carry the verdict on their own assumptions, sample sizes verified by re-evaluating power, reproducible resampling, and a critical value that is absent rather than clamped.
- [A/B testing and causal inference kernels](https://docs.algenta.ai/time-series-and-statistics/ab-testing-and-causal-inference-kernels.md): Two-arm experiment sizing and bandit selection, a compact single-formula causal-effects toolkit, classical decision theory under uncertainty, an ML-to-decision fusion layer, and five nonparametric hyp
- [Causal inference](https://docs.algenta.ai/time-series-and-statistics/causal-inference.md): Identification first — d-separation and the backdoor criterion on a declared graph — then IPW, matching, doubly-robust and panel estimation, and sensitivity analysis, refusing when not identified.
- [Survival analysis](https://docs.algenta.ai/time-series-and-statistics/survival-analysis.md): Time-to-event estimation that respects censoring — Kaplan-Meier with confidence bands, Nelson-Aalen, a kernel-smoothed hazard rate, Cox regression with Efron ties
- [Evaluation, survival, and psychometric kernels](https://docs.algenta.ai/time-series-and-statistics/ml-evaluation-survival-and-psychometric-kernels.md): Frozen hypothesis-test, survival-curve, operational-metrics, psychometric, and interval-arithmetic primitives that other kernel families call rather than reimplement.
- [Evaluation & calibration gates](https://docs.algenta.ai/time-series-and-statistics/evaluation-calibration.md): Brier with Murphy's decomposition, log-loss that says when it was clamped, calibration error that never travels without its binning, reproducible bootstrap intervals
- [Program execution & scoring](https://docs.algenta.ai/time-series-and-statistics/program-execution-scoring.md): Executes a resolved FinQA-style arithmetic program — ten operations, steps that reference earlier results by index — to the official evaluator's own rounding spec, bit-for-bit.


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