stationaritytoolkit 0.1.5

Test and Convert non-stationary time-series to stationary

📊 StationarityToolkit

StationarityToolkit is a Python library designed to help you analyze and transform time series data for stationarity. It offers a suite of statistical tests and automated transformations to detect and handle both trend and variance non-stationarity.

Whether you're building a forecasting model or preparing data for analysis, this toolkit makes your preprocessing easier and more reliable.

🚀 Features

✅ 1. Test for Variance Non-Stationarity

• Use the Phillips-Perron test to detect variance instability.

✅ 2. Test for Trend Non-Stationarity

• Use both ADF (Augmented Dickey-Fuller) and KPSS (Kwiatkowski-Phillips-Schmidt-Shin) tests to check for trend-based non-stationarity.

🔧 3. Remove Trend Non-Stationarity

• Automatically apply:
  • Trend differencing
  • Seasonal differencing
  • Or a combination of both
• Optimized for weekly seasonal data.

🔧 4. Remove Variance Non-Stationarity

• Automatically apply transformations like:
  • Logarithmic
  • Square root
  • Box-Cox
• Selects the best transformation based on statistical significance.
• Skips transformation if variance is already stationary.

🧹 5. Remove All Non-Stationarity

• Combine both variance and trend removal in one pipeline:
  • Detect and remove variance issues first
  • Then proceed to handle trend non-stationarity

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🛠️ Installation

pip install StationarityToolkit

🧪 Quick Start:

1. Import the toolkit:

    from stationarity_toolkit.stationarity_toolkit import StationarityToolkit
   

2. Initialize the Toolkit:

    toolkit = StationarityToolkit(alpha=0.05)
   

⚙️ Usage Guide

1. ✅ Test for Stationarity:

    toolkit.perform_pp_test(ts)     # Phillips-Perron test for variance non-stationarity
    toolkit.adf_test(ts)            # Augmented Dickey-Fuller test for trend
    toolkit.kpss_test(ts)           # KPSS test for trend
   

2. 🔧 Remove Variance Non-Stationarity

    toolkit.remove_var_nonstationarity(ts_as_a_dataframe)
   

• Checks if variance non-stationarity exists.
• Applies log, square root, and Box-Cox transformations.
• Selects the transformation that produces the lowest p-value.
• Skips transformation if unnecessary.

3. 🔧 Remove Trend Non-Stationarity

    toolkit.remove_var_nonstationarity(ts_as_a_dataframe)
   

• Applies differencing techniques:
  • Lag differencing
  • Seasonal differencing
  • Combination of both
• Evaluates each using ADF and KPSS tests to find the best transformation.
• ⚠️ Currently supports weekly seasonality only.

4. 🧹 Remove All Non-Stationarity

   toolkit.remove_nonstationarity(ts_as_a_dataframe)
   

• Runs both variance and trend checks/removal:
  • Removes variance non-stationarity (if present)
  • Then removes trend non-stationarity

💡 Why Stationarity Matters

• Most classical and deep learning time series models (ARIMA, VAR, Prophet, LSTM) assume that the data is stationary. Non-stationary data can lead to:
  • Spurious regressions
  • Poor model accuracy
  • Invalid statistical inferences

StationarityToolkit helps you automate this critical preprocessing step with minimal manual intervention.