Metadata-Version: 2.1
Name: spinex_timeseries
Version: 0.14
Summary: A Python package for Timeseries
Home-page: https://doi.org/10.1016/j.cie.2024.110812
Requires-Python: >=3.6
Requires-Dist: numpy>=1.26.4
Requires-Dist: pandas>=2.2.2
Requires-Dist: scikit-learn>=1.6.0
Requires-Dist: scipy>=1.14.1
Requires-Dist: matplotlib>=3.0.0
Requires-Dist: numba>=0.57.1
Requires-Dist: seaborn>=0.11.0
Requires-Dist: statsmodels>=0.13.0

Please read more about this algorithm in: https://doi.org/10.1016/j.cie.2024.110812.

At the moment, we are working diligently to improve and update SPINEX. Please share your thoughts and suggestions with us.

# Example of using this algorithm

import numpy as np

from spinex_timeseries import SPINEX_Timeseries

# Generate sample data

time = np.linspace(0, 10, 200)

data = np.cos(time) + np.random.normal(0, 0.03, 200)

# Initialize the model

model = SPINEX_Timeseries(data, forecast_horizon=20)

# Make predictions

predictions = model.predict()

print("Predicted Values:", predictions)

# Detect anomalies

anomalies, threshold = model.detect_anomalies()

print("Anomalies Detected:", anomalies)

# Plot predictions

model.plot_prediction()
