I did the 1st differencing as the time series is not stationary. When I do the invert transformation, some values are coming as negative as we get negative values due to diff(). Is there a way to sort it out and bring back the data in original format as close to the expected result.
This is my python code. Is there a way to fix the code or any alternate logic to make the data as stationary and forecasting the series?
count = 0
def invert_transformation(df_train, df_forecast):
"""Revert back the differencing to get the forecast to original scale."""
df_fc = df_forecast.copy()
columns = df_train.columns
if count > 0: # For 1st differencing
print("Enter into invert transformation")
for col in columns:
df_fc[str(col)+'_f'] = df_train[col].iloc[-1] + df_fc[str(col)+'_f'].cumsum()
print("df_fc: \n", df_fc)
return df_fc
# Since the data is not stationary, I did the 1st difference
df_differenced = df_train.diff().dropna()
count = count + 1 #increase the count
count
....
....
model = VAR(df_differenced)
....
fc = model_fitted.forecast(y=forecast_input, steps=10)
df_forecast2 = pd.DataFrame(fc, index=df2.index[-nobs:], columns=df2.columns + '_f')
df_results = invert_transformation(df_train, df_forecast2)
value of df_results(TS is the index column) are:
TS Field1_f Field2_f
44:13.0 6.826511e+05 1.198614e+06
44:14.0 -8.620101e+05 4.694556e+05
..
..
44:22.0 -1.401620e+07 -2.092826e+06
Value of df_differenced are:
TS Field1 Field2
43:34.0 187000.0 29000.0
43:35.0 175000.0 76722.0
43:36.0 -10000.0 31000.0
43:37.0 90000.0 42000.0
43:38.0 -130000.0 -42000.0
43:39.0 40000.0 -98444.0
..
..
44:11.0 -130000.0 40722.0
44:12.0 117000.0 -42444.0
