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damariushayes
time-series-velocity-baseline
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Localized Time-Series Velocity Baseline Utilities
A clean, high-performance mathematical toolkit engineered to perform time-series decomposition, outlier mitigation, and variance profiling on sparse multi-index point-of-sale datasets.
Core Features
Micro-Horizon Capacity Valuation: Pinpoints localized time windows where system velocities experience standard deviations below historical trends.
Stochastic Outlier Control: Robust preprocessing modules to isolate network latency drops from structural operational freezes.
Macro-Temporal Structural Decomposition: Uses additive multi-layered time-series filters to parse trend, cycle, and structural day-of-week closures.
Installation
pip install .
## Quick Start
You can use the baseline filter by passing a Pandas DataFrame containing your raw transaction logs. The DataFrame requires four columns: `zip_code`, `mcc`, `timestamp`, and `volume`.
```python<br>import pandas as pd<br>from micro import calculate_optimized_baseline
# 1. Create dummy transaction logs (e.g., a register processing transactions)<br>data = {<br>"zip_code": ["90210"] * 5,<br>"mcc": ["5411"] * 5, # Grocery Stores<br>"timestamp": [<br>"2026-06-28 09:00:00",<br>"2026-06-28 09:15:00",<br>"2026-06-28 09:30:00",<br>"2026-06-28 09:45:00",<br>"2026-06-28 10:00:00" # Sudden silent dropout<br>],<br>"volume": [120, 115, 130, 125, 5] # Massive sudden drop in transaction volume
df = pd.DataFrame(data)
# 2. Run the optimization baseline filter (alpha controls detection sensitivity)<br>dead_zones = calculate_optimized_baseline(df, alpha=0.25)
print(dead_zones)
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