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.
Tools and techniques for detrending time series data in macroeconomic analysis.
Install Timeseries Detrending
$ npx skills add benchflow-ai/skillsbench --skill timeseries-detrending -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench timeseries-detrending --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/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending .claude/skills/timeseries-detrending && 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 "timeseries-detrending" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending into .claude/skills/timeseries-detrending/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "timeseries-detrending", 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/benchflow-ai/skillsbench/tree/main/tasks/econ-detrending-correlation/environment/skills/timeseries-detrendingType 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 benchflow-ai/skillsbench --skill timeseries-detrending -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench timeseries-detrending --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending .agents/skills/timeseries-detrending && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "timeseries-detrending" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending into .agents/skills/timeseries-detrending/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "timeseries-detrending", 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 benchflow-ai/skillsbench --skill timeseries-detrending -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench timeseries-detrending --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending .cursor/skills/timeseries-detrending && 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 "timeseries-detrending" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending into .cursor/skills/timeseries-detrending/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "timeseries-detrending", 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/benchflow-ai/skillsbench.git --path tasks/econ-detrending-correlation/environment/skills/timeseries-detrending--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 benchflow-ai/skillsbench --skill timeseries-detrending -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench timeseries-detrending --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending .gemini/skills/timeseries-detrending && 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 "timeseries-detrending" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending into .gemini/skills/timeseries-detrending/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "timeseries-detrending", 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 benchflow-ai/skillsbench timeseries-detrendingInstalls 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 benchflow-ai/skillsbench --skill timeseries-detrending -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending .github/skills/timeseries-detrending && 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 "timeseries-detrending" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending into .github/skills/timeseries-detrending/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "timeseries-detrending", 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 benchflow-ai/skillsbench --skill timeseries-detrending -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench timeseries-detrending --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending .opencode/skills/timeseries-detrending && 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 "timeseries-detrending" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/econ-detrending-correlation/environment/skills/timeseries-detrending into .opencode/skills/timeseries-detrending/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "timeseries-detrending", 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
timeseries-detrending- GitHub stars
- 1.8k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 365 words
- Files
- 1
- Skills in repo
- 189
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Tools and techniques for detrending time series data in macroeconomic analysis.
- Works in 4 steps: Multiplicative to Additive: Converts… → Stabilizes Variance: Growth rates become… → Economic Interpretation: Cyclical… → …
- Working with economic time series that need to be decomposed into trend and cyclical components
- SKILL.md covers Overview, The Hodrick-Prescott (HP) Filter, Log Transformation for Growth… and Complete Workflow for Detrending, plus 1 more section
- Calls pip
What it does
Timeseries Detrending is an agent skill from benchflow-ai/skillsbench. Tools and techniques for detrending time series data in macroeconomic analysis. Use when working with economic time series that need to be decomposed into trend and cyclical components. Covers HP filter, log transformations for growth series, and correlation analysis of business cycles.
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Forecasting and time series. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.
When your agent uses it
- Working with economic time series that need to be decomposed into trend and cyclical components
- Tasks that involve Forecasting and time series
Example prompts
- “Use the timeseries-detrending skill to tool and techniques for detrending time series data in macroeconomic analysis”
- “/timeseries-detrending”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Multiplicative to Additive: Converts percentage changes to log differences
- Stabilizes Variance: Growth rates become comparable across time
- Economic Interpretation: Cyclical component represents percentage deviations from trend
- Standard Practice: Required for business cycle statistics that compare volatilities
What it can do on your machine
Read from SKILL.md and the folder at commit 9a1f4dd. 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
Shell commands in SKILL.md call:
pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
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
Timeseries Detrending loads about 1.2k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 365 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 benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 365 words, ~1,163 tokens.
Download SKILL.mdSave it as
.claude/skills/timeseries-detrending/SKILL.md (or your agent's skills folder).- name
- timeseries-detrending
- description
- Tools and techniques for detrending time series data in macroeconomic analysis. Use when working with economic time series that need to be decomposed into trend and cyclical components. Covers HP filter, log transformations for growth series, and correlation analysis of business cycles.
Time Series Detrending for Macroeconomic Analysis
This skill provides guidance on decomposing economic time series into trend and cyclical components, a fundamental technique in business cycle analysis.
Overview
Economic time series like GDP, consumption, and investment contain both long-term trends and short-term fluctuations (business cycles). Separating these components is essential for:
- Analyzing business cycle correlations
- Comparing volatility across variables
- Identifying leading/lagging indicators
The Hodrick-Prescott (HP) Filter
The HP filter is the most widely used method for detrending macroeconomic data. It decomposes a time series into a trend component and a cyclical component.
Mathematical Foundation
Given a time series $y_t$, the HP filter finds the trend $\tau_t$ that minimizes:
$$\sum_{t=1}^{T}(y_t - \tau_t)^2 + \lambda \sum_{t=2}^{T-1}[(\tau_{t+1} - \tau_t) - (\tau_t - \tau_{t-1})]^2$$
Where:
- First term: Minimizes deviation of data from trend
- Second term: Penalizes changes in the trend's growth rate
- $\lambda$: Smoothing parameter controlling the trade-off
Choosing Lambda (λ)
Critical: The choice of λ depends on data frequency:
| Data Frequency | Recommended λ | Rationale |
|---|---|---|
| Annual | 100 | Standard for yearly data |
| Quarterly | 1600 | Hodrick-Prescott (1997) recommendation |
| Monthly | 14400 | Ravn-Uhlig (2002) adjustment |
Common mistake: Using λ=1600 (quarterly default) for annual data produces an overly smooth trend that misses important cyclical dynamics.
Python Implementation
from statsmodels.tsa.filters.hp_filter import hpfilter
import numpy as np
# Apply HP filter
# Returns: (cyclical_component, trend_component)
cycle, trend = hpfilter(data, lamb=100) # For annual data
# For quarterly data
cycle_q, trend_q = hpfilter(quarterly_data, lamb=1600)Important: The function parameter is lamb (not lambda, which is a Python keyword).
Log Transformation for Growth Series
Show full SKILL.md (154 more words)Show less
Why Use Logs?
For most macroeconomic aggregates (GDP, consumption, investment), you should apply the natural logarithm before filtering:
- Multiplicative to Additive: Converts percentage changes to log differences
- Stabilizes Variance: Growth rates become comparable across time
- Economic Interpretation: Cyclical component represents percentage deviations from trend
- Standard Practice: Required for business cycle statistics that compare volatilities
import numpy as np
# Apply log transformation BEFORE HP filtering
log_series = np.log(real_series)
cycle, trend = hpfilter(log_series, lamb=100)
# The cycle now represents percentage deviations from trend
# e.g., cycle = 0.02 means 2% above trendWhen NOT to Use Logs
- Series that can be negative (net exports, current account)
- Series already expressed as rates or percentages
- Series with zeros
Complete Workflow for Detrending
Step-by-Step Process
- Load and clean data: Handle missing values, ensure proper time ordering
- Convert to real terms: Deflate nominal values using appropriate price index
- Apply log transformation: For positive level variables
- Apply HP filter: Use appropriate λ for data frequency
- Analyze cyclical component: Compute correlations, volatilities, etc.
Example: Business Cycle Correlation
import pandas as pd
import numpy as np
from statsmodels.tsa.filters.hp_filter import hpfilter
# Load real (inflation-adjusted) data
real_consumption = pd.Series(...) # Real consumption expenditure
real_investment = pd.Series(...) # Real fixed investment
# Log transformation
ln_consumption = np.log(real_consumption)
ln_investment = np.log(real_investment)
# HP filter with λ=100 for annual data
cycle_c, trend_c = hpfilter(ln_consumption, lamb=100)
cycle_i, trend_i = hpfilter(ln_investment, lamb=100)
# Compute correlation of cyclical components
correlation = np.corrcoef(cycle_c, cycle_i)[0, 1]
print(f"Business cycle correlation: {correlation:.4f}")Dependencies
Ensure these packages are installed:
pip install statsmodels pandas numpyThe HP filter is in statsmodels.tsa.filters.hp_filter.
© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in tasks/econ-detrending-correlation/environment/skills/timeseries-detrending of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Compare with similar skills
Timeseries Detrending 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 |
|---|---|---|---|---|---|---|
| Timeseries Detrending this skillbenchflow-ai/skillsbench | 1.8k | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| 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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Categories
Questions about Timeseries Detrending
What does Timeseries Detrending do?
Tools and techniques for detrending time series data in macroeconomic analysis. Timeseries Detrending is an agent skill from benchflow-ai/skillsbench. Tools and techniques for detrending time series data in macroeconomic analysis.
When should I use Timeseries Detrending?
Timeseries Detrending fits situations like: working with economic time series that need to be decomposed into trend and cyclical components; tasks that involve Forecasting and time series.
How do I install Timeseries Detrending in Claude Code?
Run `npx skills add benchflow-ai/skillsbench --skill timeseries-detrending -a claude-code`. Or copy the skill folder (tasks/econ-detrending-correlation/environment/skills/timeseries-detrending in benchflow-ai/skillsbench) into .claude/skills/timeseries-detrending in your project. Claude Code loads it when a task matches its description.
How do I install Timeseries Detrending in Codex?
Run `npx skills add benchflow-ai/skillsbench --skill timeseries-detrending -a codex`. Or copy the skill folder (tasks/econ-detrending-correlation/environment/skills/timeseries-detrending in benchflow-ai/skillsbench) into .agents/skills/timeseries-detrending in your project. Codex loads it when a task matches its description.
Can I use Timeseries Detrending 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 benchflow-ai/skillsbench --skill timeseries-detrending -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/timeseries-detrending, .gemini/skills/timeseries-detrending, .github/skills/timeseries-detrending and .opencode/skills/timeseries-detrending in your project.
What does Timeseries Detrending need to run?
Going by SKILL.md and its folder, Timeseries Detrending needs the command-line tools its instructions call (pip). Our summary lists: Python 3.
Does Timeseries Detrending access the network?
SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Timeseries Detrending 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 Timeseries Detrending use?
Timeseries Detrending is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Timeseries Detrending use?
About 1.2k tokens (SKILL.md is roughly 4.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Timeseries Detrending?
Skills that share tags, products or a category with Timeseries Detrending: 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 Timeseries Detrending?
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,835 GitHub stars. The repository holds 189 skills in this directory. The repository was last updated on July 23, 2026.
Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.