Perform advanced time series analysis including stationarity test, Granger causality test and prewhitening etc.

Purpose

This App can be used to analyze time series data using stationarity test, Granger causality test and prewhitening etc.

Notes: This App needs Embedded Python and statsmodels library.

Features include:

• Stationarity Test: Test stationarity in time series data. A stationary series is one in which the mean, variance and covariance do not vary with time. The Augmented Dickey-Fuller (ADF) test and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test are used.
• Granger Causality Test: Perform Granger causality test between two time series. 
• Prewhiten: Prewhiten data using AMRIMA model. Determine an ARIMA model for the x-variable and calucate its residuals. Use the ARIMA model to fit y-variable and calculate the residuals for y-variable. Their residuals are used as prewhitened data.

Installation

1. Download the advts.opx file, then drag-and-drop onto the Origin workspace.
2. The App will start downloading dependent Python libraries. Please restart Origin after the download is completed.

Operation

• Stationarity Test:
  1. Choose XY range data and enter significance level.
  2. Augmented Dickey-Fuller (ADF) test:
     • Terms to include in regression: Constant and trend order to include in regression.
     • Method to determine lag: If None, use int(12*(N/100)^(1/4)) as maximum number of lags. If AIC or BIC, then the number of lags is chosen to minimize the corresponding information criterion. If t-stat, then starts with maxlag and drops a lag until the t-statistic on the last lag length is significant using a 5% test.
  3. Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test:
     • Terms to include in regression: Constant or trend to include in regression.
     • Method to determine lag: If auto, lags is calculated using the data-dependent method. If set to legacy, uses int(12 * (N/ 100)^(1 / 4)).
  4. Click OK to output report.
• Granger Causality Test:
  1. Choose two datasets as XY time series data.
  2. On Options tab, enter Maximum Number of Lags, and Significance Level.
  3. Click OK to output report.
• Prewhiten:
  1. Choose two datasets as input.
  2. On Options tab, specify autoregressive order(p), degress of differencing(d) and moving average order(q) for nonseasonal order. You can optionally specify seasonal order by entering (p,d,q) and periodicity. Choose whether to Include a Constant Term.
  3. Click OK to output report.

Sample OPJU File
This app provides a sample OPJU file.  Right click the App icon in the Apps Gallery window, and choose Show Samples Folder from the short-cut menu. A folder will open. Drag-and-drop the project file Advanced Time Series Analysis Sample.opju from the folder onto Origin. The Notes window in the project shows detailed steps.
Note: If you wish to save the OPJU after changing, it is recommended that you save to a different folder location (e.g. User Files Folder).

Reference: https://www.statsmodels.org