trend-analysis

name	trend-analysis
version	1.1.1
description	Tests long-term time-series trends with linear regression and Mann-Kendall / Sen's slope. Use when asking whether a series is statistically increasing or decreasing over years. Not for forecasting, seasonality without detrend, change-point detection, or series shorter than 15 points.
risk	safe
source	openrouter-deepsearch
date_added	2026-06-14T00:00:00.000Z

When to Use

Use this skill when you need to determine if a time series shows a statistically significant long-term increase or decrease. Trigger keywords: trend detection, long-term trend, Mann-Kendall, Sen's slope, is it increasing, is it decreasing, statistically significant trend, time series trend.

Typical scenarios:

• Identifying long-term changes in environmental data (temperature, precipitation, air quality).
• Analyzing financial or economic market trends over multi-year horizons.
• Monitoring drift in scientific measurements or sensor readings.
• Detecting gradual shifts in operational metrics (latency, error rate, throughput).

This skill supports both parametric (linear regression) and non-parametric (Sen's slope with Mann-Kendall test) methods, giving flexibility based on data characteristics and distributional assumptions.

Do NOT use this skill for

• Data with no temporal component.
• Short-term fluctuations or seasonality without first detrending (use STL decomposition first).
• Forecasting future values (use a dedicated forecasting skill/model).
• Non-stationary processes with abrupt structural breaks not captured by a simple linear trend (use change-point detection first).
• Datasets with fewer than 15 observations (results unreliable below this threshold).
• Time series with significant autocorrelation (apply pre-whitening first).
• Data with missing values (impute or use specialized methods such as Seasonal-Trend decomposition).

Prerequisites

Python environment

• Python 3.11+
• scipy (for parametric linear regression)
• numpy
• pandas 2.0+ (for data loading and preprocessing)
• pymannkendall 2.0.0+ (for non-parametric Mann-Kendall / Sen's slope)

Install dependencies:

pip install "scipy>=1.11" "numpy>=1.24" "pandas>=2.0" "pymannkendall>=2.0.0"

Data requirements

• A time series with at least 15 observations.
• Evenly spaced time intervals (or documented irregularity).
• No missing values — impute or drop before analysis.
• For autocorrelated series, pre-whiten before applying Mann-Kendall.

Method notes in this file

This folder ships SKILL.md only. Use Step 2 (method table), the p-value table in Step 3a, and the autocorrelation pitfall below. For autocorrelated series: fit an AR(1) (or ARMA as needed), run Mann-Kendall on the residuals, and report that pre-whitening was applied.

Procedure

Step 1 — Load and validate data

import pandas as pd
import numpy as np

# Windows path example: r"~\data\precipitation.csv"
filepath = r"~\data\precipitation.csv"

df = pd.read_csv(filepath, parse_dates=["Year"], dtype={"Precipitation": np.float64})

# Hard checks
assert df["Precipitation"].isna().sum() == 0, "Missing values detected — impute before analysis"
assert len(df) >= 15, "Need at least 15 data points for reliable trend results"

values = df["Precipitation"].values
years  = df["Year"].dt.year.values  # numeric time axis

Step 2 — Choose method

Condition	Recommended method
Linear relationship, normal residuals, homoscedastic	Linear regression (scipy.stats.linregress)
Non-normal, outliers present, environmental data	Mann-Kendall + Sen's slope (pymannkendall)
Seasonal data (monthly, quarterly)	Seasonal Mann-Kendall (mk.seasonal_test)
Autocorrelated data	Pre-whiten (AR(1)/ARMA residuals), then Mann-Kendall

If unsure, use the table above: normal linear residuals → linregress; outliers / environmental → Mann-Kendall + Sen's slope; monthly/quarterly → seasonal MK.

Step 3a — Parametric: Linear Regression

from scipy import stats

slope, intercept, r_value, p_value, std_err = stats.linregress(years, values)

print(f"Slope:      {slope:.4f} units/year")
print(f"95% CI:     [{slope - 1.96*std_err:.4f}, {slope + 1.96*std_err:.4f}]")
print(f"p-value:    {p_value:.4f}")
print(f"R-squared:  {r_value**2:.4f}")

Interpretation:

p-value	Interpretation
p < 0.001	Very highly significant trend
p < 0.01	Highly significant trend
p < 0.05	Significant trend
p < 0.10	Marginally significant
p >= 0.10	No significant trend

Use the p-value table above when reporting significance to stakeholders.

Step 3b — Non-Parametric: Mann-Kendall + Sen's Slope

import pymannkendall as mk  # version 2.0.0+

result = mk.original_test(values, alpha=0.05)

print(f"Trend:        {result.trend}")        # 'increasing', 'decreasing', or 'no trend'
print(f"Sen's slope:  {result.slope:.4f} units/time_unit")
print(f"p-value:      {result.p:.4f}")
print(f"Intercept:    {result.intercept:.4f}")
print(f"CI (lower):   {result.intercept_ci[0]:.4f}")
print(f"CI (upper):   {result.intercept_ci[1]:.4f}")

Step 3c — Seasonal Mann-Kendall (for monthly/quarterly data)

result = mk.seasonal_test(values, period=12, alpha=0.05)

print(f"Trend:        {result.trend}")
print(f"Sen's slope:  {result.slope:.4f}")
print(f"p-value:      {result.p:.4f}")

Step 4 — Full analysis with error handling

import pandas as pd
import numpy as np
import pymannkendall as mk
from datetime import datetime
import json

def analyze_trend(filepath: str, column: str = "Precipitation", period: int = 12):
    """Analyze trend with comprehensive error handling."""
    try:
        df = pd.read_csv(filepath, parse_dates=["Year"], dtype={column: np.float64})

        if df[column].isna().sum() > 0:
            raise ValueError(f"Missing values detected in '{column}' column — impute before analysis")

        if len(df) < 15:
            print("Warning: Sample size < 15 may reduce result reliability")

        result = mk.original_test(df[column].values, alpha=0.05)

        report = {
            "analysis_date": datetime.now().isoformat(),
            "method": "Mann-Kendall with Sen's Slope",
            "n": len(df),
            "trend": result.trend,
            "slope": round(result.slope, 4),
            "p_value": round(result.p, 6),
            "intercept": round(result.intercept, ),
            : (result.intercept_ci[], ),
            : (result.intercept_ci[], ),
        }
         report

     FileNotFoundError:
         FileNotFoundError()
     Exception  e:
         RuntimeError()

Step 5 — Report results

Always include:

1. Method used (linear regression vs. Mann-Kendall + Sen's slope).
2. Slope estimate with units (e.g., 0.32 mm/year).
3. 95% confidence interval for the slope.
4. p-value rounded to 4 decimal places.
5. Sample size (n).
6. Trend direction (increasing / decreasing / no trend).
7. Caveats (autocorrelation, seasonality, sample size limitations).

Pitfalls

• Fewer than 15 data points — Results unreliable. The 2026 standard raises the minimum from 10 to 15 observations.
• Autocorrelation inflates significance — Mann-Kendall assumes independence. If autocorrelation is present, pre-whiten the series first (AR(1) or ARMA residuals, then MK).
• Missing values silently dropped — Always check df[column].isna().sum() before analysis. Do not let pandas silently drop rows.
• Confusing Sen's slope with regression slope — Sen's slope is the median of pairwise slopes; it is robust to outliers but not directly comparable to OLS slope.
• Using mk.original_test on seasonal data — Use mk.seasonal_test() with the correct period parameter instead.
• pymannkendall version < 2.0.0 — The alpha parameter and enhanced tests require version 2.0.0+. Verify with pip show pymannkendall.
• Not reporting confidence intervals — Always report CIs alongside point estimates; a slope without a CI is incomplete.
• Rounding p-values too aggressively — Round to at least 4 decimal places. Report p < 0.001 rather than p = 0.000.
• Passing lists instead of numpy arrays — Use np.array() or .values for performance and correctness.
• Ignoring structural breaks — A single abrupt change can create an apparent trend. Run change-point detection first if breaks are suspected.

Verification

Run these checks after implementing the analysis:

# Confirm package versions
python -c "import scipy; print('scipy', scipy.__version__)"
python -c "import pymannkendall as mk; print('pymannkendall', mk.__version__)"
python -c "import pandas; print('pandas', pandas.__version__)"

# Verify linear regression against a known dataset
import numpy as np
from scipy import stats

years  = np.arange(2000, 2020)
values = np.arange(2000, 2020) * 0.5 + np.random.normal(0, 0.1, 20)
slope, intercept, r_value, p_value, std_err = stats.linregress(years, values)

assert p_value < 0.001, f"Expected highly significant trend, got p={p_value}"
assert abs(slope - 0.5) < 0.05, f"Slope should be ~0.5, got {slope}"
print("Linear regression verification: PASS")

# Verify Mann-Kendall on increasing, decreasing, and flat series
import numpy as np
import pymannkendall as mk

inc = np.arange(50, dtype=float) + np.random.normal(0, 0.5, 50)
dec = -np.arange(50, dtype=float) + np.random.normal(0, 0.5, 50)
flat = np.random.normal(10, 1, 50)

assert mk.original_test(inc).trend == "increasing"
assert mk.original_test(dec).trend == "decreasing"
assert mk.original_test(flat).trend == "no trend"
print("Mann-Kendall verification: PASS")

Checklist:

• scipy.stats.linregress produces correct results against known datasets.
• pymannkendall 2.0.0+ correctly classifies increasing, decreasing, and no-trend scenarios.
• Confidence intervals are calculated and reported for both methods.
• Edge cases tested: small datasets (< 15), missing values, outliers.
• Datetime objects handled correctly in trend analysis.
• Seasonal decomposition works when preprocessing data.
• Both numpy arrays and pandas Series inputs accepted.
• Error handling covers FileNotFoundError, missing values, and insufficient sample size.

Related Skills

• Time Series Forecasting — Predicting future values from historical patterns (Prophet, NeuralProphet).
• Anomaly Detection — Identifying unusual points deviating from expected trend (Isolation Forest, LSTM detectors).
• Data Visualization — Plotting time series and trend lines (Plotly, Altair for interactive charts).
• Change Point Detection — Identifying structural breaks (ruptures, Bayesian methods).
• Spatial-Temporal Analysis — Analyzing trends across both space and time dimensions.