Time Series Stationary Technique

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I am working with time series data (non-stationary), I have applied .diff(periods=n) for differencing the data to eliminate trends and seasonality factors from data.

By using .diff(periods=n), the observation from the previous time step (t-1) is subtracted from the current observation (t).

Now I want to invert back the differenced data to its original scale, but I am having issues with that. You can find the code here.

My code for differencing:

data_diff = df.diff(periods=1)     

data_diff.head(5)

My code for inverting the differenced data back to its original scale:

cols = df.columns
x = []
for col in cols:
    diff_results = df[col] + data_diff[col].shift(-1)
    x.append(diff_results)
diff_df_inverted = pd.concat(x, axis=1)

diff_df_inverted

As you can see from last output in the code, I have successfully inverted my data back to its original scale. However, I do not get the inverted data for row 1. It inverts and shifts the values up a row. My question is, why? What am I missing?

thank you!

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1 Answers

In this line:

diff_results = df[col] + data_diff[col].shift(-1)

data_diff starts from the second row and that is the reason it appears as it could be shifted up.
The reason for this is because you use .shift(-1).

An easy solution would be using df.cumsum() as it is the exact opposite of df.diff().

The only thing you have to do is get the first row to replace the NaN values from your data_diff dataframe. You need to do this because it is the original row that every other row would be added to. After that, you call data_diff.cumsum() and now you have the original data.

Here is the detailed code.

data_diff.iloc[0]=df.iloc[0]
a = data_diff.cumsum()
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