Skillshub analyzing-time-series

Comprehensive diagnostic analysis of time series data. Use when users provide CSV time series data and want to understand its characteristics before forecasting - stationarity, seasonality, trend, forecastability, and transform recommendations.

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/https-deeplearning-ai/sc-agent-skills-files/analyzing-time-series" ~/.claude/skills/comeonoliver-skillshub-analyzing-time-series-6c2c4b && rm -rf "$T"

manifest: skills/https-deeplearning-ai/sc-agent-skills-files/analyzing-time-series/SKILL.md

source content

Time Series Diagnostics

Comprehensive diagnostic toolkit to analyze time series data characteristics before forecasting.

Input Format

The input CSV file should have two columns:

Date column - Timestamps or dates (e.g.,
```
date
```
,
```
timestamp
```
,
```
time
```
)
Value column - Numeric values to analyze (e.g.,
```
value
```
,
```
sales
```
,
```
temperature
```
)

Workflow

Step 1: Run diagnostics

python scripts/diagnose.py data.csv --output-dir results/

This runs all statistical tests and analyses. Outputs

diagnostics.json

with all metrics and

summary.txt

with human-readable findings. Column names are auto-detected, or can be specified with

--date-col

and

--value-col

options.

Step 2: Generate plots (optional)

python scripts/visualize.py data.csv --output-dir results/

Creates diagnostic plots in

results/plots/

for visual inspection. Run after

diagnose.py

to ensure ACF/PACF plots are synchronized with stationarity results. Column names are auto-detected, or can be specified with

--date-col

and

--value-col

options.

Step 3: Report to user

Summarize findings from

summary.txt

and present relevant plots. See

references/interpretation.md

for guidance on:

Is the data forecastable?
Is it stationary? How much differencing is needed?
Is there seasonality? What period?
Is there a trend? What direction?
Is a transform needed?

Script Options

Both scripts accept:

```
--date-col NAME
```
- Date column (auto-detected if omitted)
```
--value-col NAME
```
- Value column (auto-detected if omitted)
```
--output-dir PATH
```
- Output directory (default:
```
diagnostics/
```
)
```
--seasonal-period N
```
- Seasonal period (auto-detected if omitted)

Output Files

results/
├── diagnostics.json       # All test results and statistics
├── summary.txt            # Human-readable findings
├── diagnostics_state.json # Internal state for plot synchronization
└── plots/
    ├── timeseries.png
    ├── histogram.png
    ├── rolling_stats.png
    ├── box_by_dayofweek.png  # By day of week (if applicable)
    ├── box_by_month.png      # By month (if applicable)
    ├── box_by_quarter.png    # By quarter (if applicable)
    ├── acf_pacf.png
    ├── decomposition.png
    └── lag_scatter.png

References

See

references/interpretation.md

for:

Statistical test thresholds and interpretation
Seasonal period guidelines by data frequency
Transform recommendations

Dependencies

pandas

numpy

matplotlib

statsmodels

scipy