forecasting
Forecast time series and impute missing values with hyp.predict, hyp.impute, and reusable fitted models.
- License 许可
- MIT
- 最近更新
- 2026-09-03
- Stars
- ★ 351
- Forks
- 27
01 前言 Front Matter
- name
- forecasting
- license
- MIT
- metadata
- { "disco-role": "operating" }
- description
- Forecast time series and impute missing values with hyp.predict, hyp.impute, and reusable fitted models.
- disable-model-invocation
- true
02 资源文件 4
- 参考文档references/forecast-reference.md8.1 KB
- 参考文档references/troubleshooting.md4.5 KB
- 参考文档references/workflows.md4.0 KB
- 脚本scripts/smoke_forecasting.py1.7 KB
03 SKILL.md 正文
Forecasting
Use this sub-skill for:
- forecasting future rows with
hyp.predict - filling missing values with
hyp.impute hyp.plot(..., predict=..., t=...)forecast overlays on static plotsreturn_model=Truereuse for fitted forecasters and imputers
Start here:
Operational rules:
- Prefer
hyp.predict(data, model=..., t=..., return_model=...)for future rows andhyp.impute(data, model=..., return_model=...)for missing values. - Treat 1-D arrays, flat lists, and
pandas.Seriesas univariate(n, 1)series. Tuples behave like lists. - Reuse fitted objects by passing the returned forecaster/imputer back as
model=on new data. - Keep forecast styling, dashed tails, colors, and legends in
../visualization/. - Keep loading/saving and source data ingestion in
../io/. - Keep generic stage-order questions (
manip/normalize/reduce/align/cluster) in../pipeline/. - Do not combine
predict=withanimate=; forecast overlays are static-only in this release. - Treat
LaplaceandChronosas optional add-on models; use base-install models first.
Model families this sub-skill covers:
- Forecasting:
Kalman,GaussianProcess,AutoRegressor,ARIMA,Laplace,Chronos - Imputation:
PPCA,SimpleImputer,KNNImputer,IterativeImputer,Kalman
If the task is really about plotting style or layout, go to ../visualization/.
If the task is about loading/saving datasets or artifacts, go to ../io/.
If the task is about the broader pipeline order, go to ../pipeline/.
04 同仓库更多 Skills
passnet-triton-opt
Make a single PassNet Triton kernel fast AND numerically faithful: performance model (when a replacement can win at all), block/grid/warp tuning, autotune policy, launch overhead, and per-op numeric recipes to pass the dtype baseline tolerances. Use when a pass matches and is correct but speedup below expected, or when correctness fails by small numeric margins.
passnet-solve
END-TO-END playbook for solving one PassNet sample: analyze the computation graphs, decide the optimization strategy (what to fuse, what to replace, what to leave alone), drive the iteration loop, and maximize the sample score. This is the ENTRY skill — invoke it first for any PassNet optimization task; it tells you when to use passnet-pattern-fusion, passnet-triton-opt and passnet-feedback.
passnet-skill
PassNet GPU kernel optimization via compiler passes. Design and implement Triton-based optimization passes, create pass files under ./pass_dir/, self-evaluate with pass_evaluator, and iterate to maximize GPU speedup.
passnet-pattern-fusion
Author PassNet pass files: write patterns that actually MATCH the FX graph, pick fusion regions (which ops to absorb into one Triton kernel), and structure multi-pass submissions with the mandatory shared-dispatch architecture. Use when creating or fixing pattern()/replacement_args()/replacement_func() files, when a pass "failed to match", or when deciding how to fuse multiple kernels/ops.
passnet-orchestrate
Round/batch planning and decision support for PassNet graph optimization. Use for multi-sample triage, worker allocation, eval-budget policy, and post-eval keep/revert decisions. For solving one concrete sample end to end, use passnet-solve as the entry skill; consult this skill only when you need broader planning or a specific decision gate.
passnet-feedback
Fast, deterministic feedback for PassNet work: pre-flight pattern/match verification WITHOUT burning a GPU evaluation, per-node bottleneck analysis of a sample's graphs, and eval-log parsing into per-variant status + estimated score + failure classification. Use BEFORE every GPU evaluation (check_pattern), at the START of a sample (analyze_graph), and AFTER every evaluation (parse_eval_log).