TSGap

A Python library for simulating realistic missingness in time-series data for imputation benchmarking.

TSGap separates mechanisms (why data is missing: MCAR, MAR, MNAR) from patterns (how data is missing: pointwise, block, monotone, decay, markov). Any mechanism can be combined with any pattern, making it easy to create controlled missing-data scenarios for evaluating imputation methods.

Features

• MCAR, MAR, and MNAR missingness mechanisms
• Pointwise, block, monotone, temporal decay, and Markov-chain patterns
• 2D (time, features) and 3D (samples, time, features) arrays
• Exact or calibrated missing-rate control
• Weighted multi-driver MAR
• Reproducible masks with NumPy random seeds
• Existing NaNs and target dimensions preserved across patterns

Installation

pip install tsgap

For development:

git clone https://github.com/feruzoripov/tsgap.git
cd tsgap
pip install -e ".[dev]"

Requires Python 3.9 or newer and NumPy 1.19 or newer.

Quick Start

import numpy as np
from tsgap import simulate_missingness

X = np.random.default_rng(42).standard_normal((1000, 6))

# MCAR: 15% scattered missing values
X_miss, mask = simulate_missingness(X, "mcar", 0.15, seed=42)

# MAR: missingness driven by dimension 0 and arranged in blocks
X_miss, mask = simulate_missingness(
    X, "mar", 0.25, seed=42,
    pattern="block", driver_dims=[0], block_len=10
)

# Scale block length to the sequence length for long series
# The default block pattern uses block_len=10 and block_density=1.0.
# block_frac is recommended
# for long wearable-style series where fixed 10-sample blocks are too short.
X_miss, mask = simulate_missingness(
    X, "mcar", 0.20, seed=42,
    pattern="block", block_frac=0.01
)

# Sample each block length between 1% and 5% of the time axis
X_miss, mask = simulate_missingness(
    X, "mcar", 0.20, seed=42,
    pattern="block", block_frac=(0.01, 0.05)
)

# MNAR: extreme values missing with monotone dropout
X_miss, mask = simulate_missingness(
    X, "mnar", 0.20, seed=42,
    pattern="monotone", mnar_mode="extreme"
)

print(f"Actual missing rate: {(~mask).mean():.4f}")

The returned mask uses:

mask == True  -> observed
mask == False -> missing

Documentation

• Installation
• Core concepts
• Mechanisms
• Patterns
• API reference
• Benchmarking workflow

Example Use Cases

• Random telemetry loss in sensor networks
• Activity-dependent dropout in wearable devices
• Participant dropout in longitudinal or clinical studies
• Gradual signal loss from sensor degradation
• Intermittent connectivity failures with bursty missingness

Evaluation Workflow

import numpy as np
from tsgap import simulate_missingness

X = np.random.default_rng(42).standard_normal((1000, 6))
X_miss, mask = simulate_missingness(X, "mar", 0.20, seed=42, driver_dims=[0])

X_imputed = your_imputation_method(X_miss)
missing_idx = ~mask

rmse = np.sqrt(np.mean((X[missing_idx] - X_imputed[missing_idx]) ** 2))
mae = np.mean(np.abs(X[missing_idx] - X_imputed[missing_idx]))

See docs/benchmarking.md for a complete baseline workflow.

The repository also includes a runnable benchmark example:

python examples/benchmark_imputation.py

Testing

pytest tsgap/tests/ -v

Citation

@software{tsgap,
  author = {Oripov, Feruz and Korchagina, Kseniia and Bonsu, Enock Adu and Bilgin, Ali and Aras, Shravan},
  title = {TSGap: A Python Library for Composable Time-Series Missingness Simulation},
  year = {2026},
  url = {https://github.com/feruzoripov/tsgap}
}

License

MIT