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AI & MACHINE LEARNING PROGRAM • LEVEL 19 — SEQUENCES & TIME SERIES

Difference a Time Series with Python

Learn difference a time series with python with a short, executable Python example.

IntermediateDifferencingStationarity

PROBLEM UNDERSTANDING

Input and expected output

Sample input
No input required
Sample output
[3, 5, 2]

COMPLETE PYTHON PROGRAM

Complete Python implementation

ai-ml-19-differencing.py
Open in compiler
values=[10,13,18,20]
print([current-previous for previous,current in zip(values,values[1:])])

GUIDED CODE TOUR • NOT LIVE EXECUTION

Study the program line by line

Use the real compiler button above to run and debug with different inputs.

CURRENT STEP

Select Start to walk through the important lines.

SELECTED LINE

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EXPECTED OUTPUT FOR THE SAMPLE

[3, 5, 2]
0%Step 0 of 0

PROGRAM EXPLANATION

Algorithm and explanation

  1. Initialize the sample values used to difference a time series.
  2. Process the data step by step using Differencing and Stationarity.
  3. Display the result for difference a time series and compare it with the documented sample output.

This example of difference a time series processes the sample values in a controlled iteration. It demonstrates Differencing and Stationarity and prints a deterministic result that can be checked against the sample output.

EFFICIENCY

Time and space complexity

Time complexity

O(n)

Auxiliary space

O(n)

DEBUGGING CHECKLIST

Common mistakes

Check this

For difference a time series, keep the data shape and value types consistent with Differencing.

Check this

Keep every dependent statement inside the correct indented Python block.

Check this

Verify the final Differencing and Stationarity result against the sample before trying new data.

Try it yourself

Practice: Run the program with the sample input, predict its output, and then test one boundary case of your own.