TimesFM Forecasting
google-research/timesfm
Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.
Combine multiple forecasting models into ensemble predictions for improved accuracy.
Install Algo Forecast Ensemble
$ npx skills add asgard-ai-platform/skills --skill algo-forecast-ensemble -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-ensemble --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/algo-forecast-ensemble .claude/skills/algo-forecast-ensemble && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "algo-forecast-ensemble" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-ensemble into .claude/skills/algo-forecast-ensemble/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-ensemble", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-ensembleType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add asgard-ai-platform/skills --skill algo-forecast-ensemble -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-ensemble --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/algo-forecast-ensemble .agents/skills/algo-forecast-ensemble && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algo-forecast-ensemble" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-ensemble into .agents/skills/algo-forecast-ensemble/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-ensemble", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-forecast-ensemble -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-ensemble --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/algo-forecast-ensemble .cursor/skills/algo-forecast-ensemble && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "algo-forecast-ensemble" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-ensemble into .cursor/skills/algo-forecast-ensemble/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-ensemble", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path algo-forecast-ensemble--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add asgard-ai-platform/skills --skill algo-forecast-ensemble -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-ensemble --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/algo-forecast-ensemble .gemini/skills/algo-forecast-ensemble && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "algo-forecast-ensemble" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-ensemble into .gemini/skills/algo-forecast-ensemble/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-ensemble", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install asgard-ai-platform/skills algo-forecast-ensembleInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add asgard-ai-platform/skills --skill algo-forecast-ensemble -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/algo-forecast-ensemble .github/skills/algo-forecast-ensemble && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "algo-forecast-ensemble" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-ensemble into .github/skills/algo-forecast-ensemble/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-ensemble", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill algo-forecast-ensemble -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-ensemble --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/algo-forecast-ensemble .opencode/skills/algo-forecast-ensemble && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "algo-forecast-ensemble" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-ensemble into .opencode/skills/algo-forecast-ensemble/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-ensemble", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
algo-forecast-ensemble- GitHub stars
- 242
- Token cost
- ~1.1k tokens
- SKILL.md length
- 380 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Combine multiple forecasting models into ensemble predictions for improved accuracy.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to improve forecast reliability
- SKILL.md covers Overview, When to Use, Algorithm and Output Format, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algo Forecast Ensemble is an agent skill from asgard-ai-platform/skills. Combine multiple forecasting models into ensemble predictions for improved accuracy. Use this skill when the user needs to improve forecast reliability, combine ARIMA/Prophet/ETS outputs, or build a robust forecasting pipeline — even if they say 'combine forecasts', 'model averaging', or 'which forecast should I trust'.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/combination-survey.md` and `references/stacking.md`).
It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs to improve forecast reliability
- Combine ARIMA/Prophet/ETS outputs
- Build a robust forecasting pipeline — even if they say combine forecasts
- Model averaging
Example prompts
- “combine forecasts”
- “model averaging”
- “which forecast should I trust”
- “/algo-forecast-ensemble”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Algo Forecast Ensemble loads about 1.1k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 86 tokens; SKILL.md has 380 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 380 words, ~1,091 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-forecast-ensemble/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-forecast-ensemble
- description
- Combine multiple forecasting models into ensemble predictions for improved accuracy. Use this skill when the user needs to improve forecast reliability, combine ARIMA/Prophet/ETS outputs, or build a robust forecasting pipeline — even if they say 'combine forecasts', 'model averaging', or 'which forecast should I trust'.
- metadata.category
- WP-47 時間序列預測
- metadata.tags
- forecasting, ensemble, model-combination, accuracy
Ensemble Forecasting
Overview
Ensemble forecasting combines predictions from multiple models to reduce variance and improve accuracy. Simple average of 3-5 diverse models often outperforms the best individual model. Methods: equal-weight average, inverse-error weighting, stacking with a meta-learner. The "forecast combination puzzle" shows simple averaging is hard to beat.
When to Use
Trigger conditions:
- Multiple forecasting models are available and perform similarly
- Reducing forecast risk is more important than maximum accuracy
- Building a production pipeline that's robust to model failure
When NOT to use:
- When one model clearly dominates all others (just use that model)
- When computational budget only allows one model
Algorithm
IRON LAW: Simple Average Often Beats Complex Combination
The "forecast combination puzzle" (Stock & Watson, 2004): equal-weight
averaging of diverse models frequently outperforms sophisticated
weighting schemes. This is because weight estimation introduces noise
that offsets the theoretical gain. Start with simple average and only
move to weighted combination if you have abundant validation data.Phase 1: Input Validation
Generate forecasts from 3+ diverse models (e.g., ARIMA, ETS, Prophet, ML-based). Ensure models are truly diverse (different assumptions/approaches). Gate: 3+ model forecasts available, models use different methodologies.
Phase 2: Core Algorithm
Simple average: ŷ_ensemble = (1/M) × Σ ŷ_m
Inverse-error weighting: w_m = (1/MSE_m) / Σ(1/MSE_j), ŷ_ensemble = Σ w_m × ŷ_m
Stacking: Train a meta-model (linear regression) that learns optimal weights from cross-validated individual model predictions.
Phase 3: Verification
Compare ensemble vs individual models on held-out data. Ensemble should: have lower average error AND lower maximum error (more robust). Gate: Ensemble RMSE ≤ best individual model RMSE.
Phase 4: Output
Return ensemble forecast with component model contributions.
Output Format
{
"ensemble_forecast": [{"period": "2025-04", "forecast": 1200, "lower_95": 1050, "upper_95": 1350}],
"model_forecasts": {"arima": 1180, "prophet": 1220, "ets": 1200},
"weights": {"arima": 0.35, "prophet": 0.30, "ets": 0.35},
"metadata": {"method": "inverse_error_weighted", "ensemble_rmse": 42, "best_individual_rmse": 48}
}Examples
Sample I/O
Input: ARIMA forecast=1180, Prophet=1220, ETS=1200 for next month sales Expected: Simple average = 1200. If ARIMA historically best (lowest MSE), weighted average shifts toward 1180.
Show full SKILL.md (144 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| All models agree | Ensemble = individual | Consensus, high confidence |
| Models wildly disagree | Ensemble = compromise, wide CI | High uncertainty, flag for review |
| One model is outlier | Average dampens outlier | Ensemble robustness benefit |
Gotchas
- Diversity is key: Combining 5 ARIMA variants adds little. Combine fundamentally different approaches (statistical + ML + judgmental).
- Weight instability: Optimal weights estimated on past data may not be optimal in the future. Simple average avoids this instability.
- Correlation between errors: If model errors are correlated (they often are), ensemble improvement is limited. Seek models with uncorrelated errors.
- Confidence intervals: Combining point forecasts is easy. Combining prediction intervals properly requires knowledge of error correlation structure.
- Over-engineering risk: For stable, well-understood series, a single well-tuned model may outperform an ensemble. Ensembles shine for uncertain or volatile series.
References
- For forecast combination methods survey, see
references/combination-survey.md - For stacking meta-learner implementation, see
references/stacking.md
© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in algo-forecast-ensemble of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/combination-survey.md
- references/stacking.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Forecast Ensemble next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Algo Forecast Ensemble this skillasgard-ai-platform/skills | 242 | — | ~1.1k | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Timesfm ForecastingzLanqing/codex-claude-academic-skills | 4.7k | 3 repos | ~7.5k | Automated safety check: Notes | Apache-2.0 | |
| Find Hypertable Candidatestimescale/pg-aiguide | 1.9k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Pensieve Searcharkohut/pensieve | 1.4k | — | ~8.2k | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Algo Forecast Ensemble
What does Algo Forecast Ensemble do?
Combine multiple forecasting models into ensemble predictions for improved accuracy. Algo Forecast Ensemble is an agent skill from asgard-ai-platform/skills. Combine multiple forecasting models into ensemble predictions for improved accuracy.
When should I use Algo Forecast Ensemble?
Algo Forecast Ensemble fits situations like: the user needs to improve forecast reliability; combine ARIMA/Prophet/ETS outputs; build a robust forecasting pipeline — even if they say combine forecasts; model averaging.
How do I install Algo Forecast Ensemble in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-forecast-ensemble -a claude-code`. Or copy the skill folder (algo-forecast-ensemble in asgard-ai-platform/skills) into .claude/skills/algo-forecast-ensemble in your project. Claude Code loads it when a task matches its description.
How do I install Algo Forecast Ensemble in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-forecast-ensemble -a codex`. Or copy the skill folder (algo-forecast-ensemble in asgard-ai-platform/skills) into .agents/skills/algo-forecast-ensemble in your project. Codex loads it when a task matches its description.
Can I use Algo Forecast Ensemble in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill algo-forecast-ensemble -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algo-forecast-ensemble, .gemini/skills/algo-forecast-ensemble, .github/skills/algo-forecast-ensemble and .opencode/skills/algo-forecast-ensemble in your project.
What does Algo Forecast Ensemble need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Forecast Ensemble is instructions for the agent only.
Does Algo Forecast Ensemble access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Algo Forecast Ensemble safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Algo Forecast Ensemble use?
Algo Forecast Ensemble is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Algo Forecast Ensemble use?
About 1.1k tokens (SKILL.md is roughly 4.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.1k tokens, read only when the agent opens those files.
What are the alternatives to Algo Forecast Ensemble?
Skills that share tags, products or a category with Algo Forecast Ensemble: TimesFM Forecasting (google-research/timesfm, 34k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars), Timesfm Forecasting (zLanqing/codex-claude-academic-skills, 4.7k stars) and Find Hypertable Candidates (timescale/pg-aiguide, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algo Forecast Ensemble?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.