| import pandas as pd |
| import numpy as np |
| import os |
| from statsmodels.tsa.stattools import adfuller |
| from arch.unitroot import ADF |
|
|
| def calculate_ADF(root_path,data_path): |
| df_raw = pd.read_csv(os.path.join(root_path,data_path)) |
| cols = list(df_raw.columns) |
| cols.remove('date') |
| df_raw = df_raw[cols] |
| adf_list = [] |
| for i in cols: |
| df_data = df_raw[i] |
| adf = adfuller(df_data, maxlag = 1) |
| print(adf) |
| adf_list.append(adf) |
| return np.array(adf_list) |
|
|
| def calculate_target_ADF(root_path,data_path,target='OT'): |
| df_raw = pd.read_csv(os.path.join(root_path,data_path)) |
| target_cols = target.split(',') |
| |
| df_raw = df_raw[target_cols] |
| adf_list = [] |
| for i in target_cols: |
| df_data = df_raw[i] |
| adf = adfuller(df_data, maxlag = 1) |
| |
| adf_list.append(adf) |
| return np.array(adf_list) |
|
|
| def archADF(root_path, data_path): |
| df = pd.read_csv(os.path.join(root_path,data_path)) |
| cols = df.columns[1:] |
| stats = 0 |
| for target_col in cols: |
| series = df[target_col].values |
| adf = ADF(series) |
| stat = adf.stat |
| stats += stat |
| return stats/len(cols) |
|
|
| if __name__ == '__main__': |
|
|
| |
| ADFmetric = archADF(root_path="./dataset/exchange_rate/",data_path="exchange_rate.csv") |
| print("Exchange ADF metric", ADFmetric) |
|
|
| |
| ADFmetric = archADF(root_path="./dataset/illness/",data_path="national_illness.csv") |
| print("Illness ADF metric", ADFmetric) |
|
|
| |
| ADFmetric = archADF(root_path="./dataset/ETT-small/",data_path="ETTm2.csv") |
| print("ETTm2 ADF metric", ADFmetric) |
|
|
| |
| ADFmetric = archADF(root_path="./dataset/electricity/",data_path="electricity.csv") |
| print("Electricity ADF metric", ADFmetric) |
|
|
| |
| ADFmetric = archADF(root_path="./dataset/traffic/",data_path="traffic.csv") |
| print("Traffic ADF metric", ADFmetric) |
|
|
| |
| ADFmetric = archADF(root_path="./dataset/weather/",data_path="weather.csv") |
| print("Weather ADF metric", ADFmetric) |
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