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.
Build ARIMA models for time series forecasting with trend and seasonality decomposition.
Install Algo Forecast Arima
$ npx skills add asgard-ai-platform/skills --skill algo-forecast-arima -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-arima --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-arima .claude/skills/algo-forecast-arima && 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-arima" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-arima into .claude/skills/algo-forecast-arima/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-arima", 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-arimaType 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-arima -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-arima --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-arima .agents/skills/algo-forecast-arima && 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-arima" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-arima into .agents/skills/algo-forecast-arima/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-arima", 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-arima -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-arima --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-arima .cursor/skills/algo-forecast-arima && 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-arima" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-arima into .cursor/skills/algo-forecast-arima/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-arima", 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-arima--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-arima -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills algo-forecast-arima --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-arima .gemini/skills/algo-forecast-arima && 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-arima" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-arima into .gemini/skills/algo-forecast-arima/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-arima", 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-arimaInstalls 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-arima -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-arima .github/skills/algo-forecast-arima && 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-arima" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-arima into .github/skills/algo-forecast-arima/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-arima", 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-arima -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-arima --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-arima .opencode/skills/algo-forecast-arima && 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-arima" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/algo-forecast-arima into .opencode/skills/algo-forecast-arima/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algo-forecast-arima", 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-arima- GitHub stars
- 242
- Token cost
- ~1.2k tokens
- SKILL.md length
- 457 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Build ARIMA models for time series forecasting with trend and seasonality decomposition.
- Works in 4 steps: Input Validation → Core Algorithm → Verification → …
- The user needs to forecast future values from historical sequential data
- 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 Arima is an agent skill from asgard-ai-platform/skills. Build ARIMA models for time series forecasting with trend and seasonality decomposition. Use this skill when the user needs to forecast future values from historical sequential data, test for stationarity, or select ARIMA parameters — even if they say 'time series forecast', 'predict next month sales', or 'ARIMA model'.
Its SKILL.md is about 1.2k 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/acf-pacf.md` and `references/seasonal-arima.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 forecast future values from historical sequential data
- Test for stationarity
- Select ARIMA parameters — even if they say time series forecast
- Predict next month sales
Example prompts
- “time series forecast”
- “predict next month sales”
- “ARIMA model”
- “/algo-forecast-arima”
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 Arima loads about 1.2k tokens when it runs, and up to ~7.9k if it reads all its reference files. Until then it costs about 85 tokens; SKILL.md has 457 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). 457 words, ~1,157 tokens.
Download SKILL.mdSave it as
.claude/skills/algo-forecast-arima/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- algo-forecast-arima
- description
- Build ARIMA models for time series forecasting with trend and seasonality decomposition. Use this skill when the user needs to forecast future values from historical sequential data, test for stationarity, or select ARIMA parameters — even if they say 'time series forecast', 'predict next month sales', or 'ARIMA model'.
- metadata.category
- WP-47 時間序列預測
- metadata.tags
- forecasting, arima, time-series, statistics
ARIMA Time Series Model
Overview
ARIMA(p,d,q) combines autoregression (AR), differencing (I), and moving average (MA) for time series forecasting. Seasonal variant: SARIMA(p,d,q)(P,D,Q,s). Requires stationary data (achieved through differencing). Best for univariate series with clear trend/seasonality patterns.
When to Use
Trigger conditions:
- Forecasting univariate time series (sales, demand, traffic)
- Data has clear trend and/or seasonal patterns
- Need interpretable model with statistical properties
When NOT to use:
- For multivariate forecasting with many external features (use ML models)
- For very long-range forecasts (ARIMA confidence intervals widen rapidly)
- For irregular/event-driven data (use causal models)
Algorithm
IRON LAW: ARIMA Requires STATIONARY Data
Non-stationary data (trend, changing variance) violates ARIMA assumptions.
Test stationarity with ADF test (p < 0.05 = stationary).
If non-stationary: difference the series (d=1 usually suffices).
If still non-stationary after d=2, ARIMA may not be appropriate.Phase 1: Input Validation
Check: regular time intervals, no missing values (impute if needed), minimum 50 observations (ideally 2+ full seasonal cycles). Test stationarity with ADF test. Gate: Data is regular, sufficient length, stationarity assessed.
Phase 2: Core Algorithm
- Stationarity: ADF test. If p > 0.05, difference (d=1). Retest.
- Parameter selection: Examine ACF/PACF plots. Or use auto_arima (AIC-based grid search).
- p (AR terms): PACF cutoff lag
- q (MA terms): ACF cutoff lag
- d: number of differences needed
- Fit model: Maximum likelihood estimation
- Forecast: Generate predictions with confidence intervals
Phase 3: Verification
Check residuals: should be white noise (no autocorrelation). Ljung-Box test (p > 0.05 = no autocorrelation). Residuals normally distributed. Gate: Residuals pass Ljung-Box test, no remaining patterns.
Phase 4: Output
Return forecasts with confidence intervals.
Output Format
{
"forecasts": [{"period": "2025-04", "forecast": 1250, "lower_95": 1100, "upper_95": 1400}],
"model": {"order": [1,1,1], "seasonal_order": [1,1,1,12], "aic": 520.3},
"metadata": {"training_periods": 60, "forecast_horizon": 12}
}Examples
Sample I/O
Input: 12 monthly observations with upward trend: [10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32]
Step 1: First difference = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] (constant → stationary, d=1 sufficient)
Step 2: ARIMA(0,1,0) random walk with drift μ=2 is the simplest fitting model.
Expected forecast (ARIMA(0,1,0) with drift=2):
- Period 13: 32 + 2 = 34
- Period 14: 32 + 4 = 36
- Period 15: 32 + 6 = 38
Verify: differenced series is constant (2) → no AR/MA terms needed. Residuals are exactly 0 → perfect fit (toy example). On real data, residuals should pass Ljung-Box (p > 0.05).
Show full SKILL.md (136 more words)Show less
Edge Cases
| Input | Expected | Why |
|---|---|---|
| No trend, no seasonality | ARIMA(p,0,q) | No differencing needed |
| Strong trend only | ARIMA(p,1,q) | Single difference removes linear trend |
| Multiple seasonalities | ARIMA may struggle | Consider Prophet or TBATS instead |
Gotchas
- Over-differencing: d=2 when d=1 suffices introduces unnecessary noise. Check if first difference is stationary before differencing again.
- Auto-ARIMA isn't magic: AIC-based selection can pick overfit models. Always check residual diagnostics regardless of auto selection.
- Confidence intervals widen fast: Multi-step forecasts accumulate uncertainty. Don't trust point forecasts beyond 2-3 seasonal cycles.
- Calendar effects: Business days, holidays, and leap years affect monthly/weekly data. ARIMA doesn't handle these natively — add regressors or use Prophet.
- Structural breaks: ARIMA assumes the data-generating process is stable. COVID, market shocks, or policy changes break this assumption.
References
- For ACF/PACF interpretation guide, see
references/acf-pacf.md - For SARIMA seasonal parameter selection, see
references/seasonal-arima.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-arima of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/acf-pacf.md
- references/seasonal-arima.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Algo Forecast Arima 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 Arima this skillasgard-ai-platform/skills | 242 | — | ~1.2k | 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 Arima
What does Algo Forecast Arima do?
Build ARIMA models for time series forecasting with trend and seasonality decomposition. Algo Forecast Arima is an agent skill from asgard-ai-platform/skills. Build ARIMA models for time series forecasting with trend and seasonality decomposition.
When should I use Algo Forecast Arima?
Algo Forecast Arima fits situations like: the user needs to forecast future values from historical sequential data; test for stationarity; select ARIMA parameters — even if they say time series forecast; predict next month sales.
How do I install Algo Forecast Arima in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill algo-forecast-arima -a claude-code`. Or copy the skill folder (algo-forecast-arima in asgard-ai-platform/skills) into .claude/skills/algo-forecast-arima in your project. Claude Code loads it when a task matches its description.
How do I install Algo Forecast Arima in Codex?
Run `npx skills add asgard-ai-platform/skills --skill algo-forecast-arima -a codex`. Or copy the skill folder (algo-forecast-arima in asgard-ai-platform/skills) into .agents/skills/algo-forecast-arima in your project. Codex loads it when a task matches its description.
Can I use Algo Forecast Arima 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-arima -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-arima, .gemini/skills/algo-forecast-arima, .github/skills/algo-forecast-arima and .opencode/skills/algo-forecast-arima in your project.
What does Algo Forecast Arima need to run?
SKILL.md names no scripts, command-line tools or credentials: Algo Forecast Arima is instructions for the agent only.
Does Algo Forecast Arima 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 Arima 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 Arima use?
Algo Forecast Arima 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 Arima use?
About 1.2k tokens (SKILL.md is roughly 4.6k 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 6.7k tokens, read only when the agent opens those files.
What are the alternatives to Algo Forecast Arima?
Skills that share tags, products or a category with Algo Forecast Arima: 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 Arima?
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.