AI & MACHINE LEARNING PROGRAM • LEVEL 19 — SEQUENCES & TIME SERIES
Apply Exponential Smoothing with Python
Learn apply exponential smoothing with python with a short, executable Python example.
PROBLEM UNDERSTANDING
Input and expected output
No input required
[10, 12.0, 12.5]
COMPLETE PYTHON PROGRAM
Complete Python implementation
values=[10,14,13]
alpha=0.5
smoothed=[values[0]]
for value in values[1:]:
smoothed.append(alpha*value+(1-alpha)*smoothed[-1])
print(smoothed)CURRENT STEP
Select Start to walk through the important lines.
SELECTED LINE
No line selected
EXPECTED OUTPUT FOR THE SAMPLE
[10, 12.0, 12.5]
PROGRAM EXPLANATION
Algorithm and explanation
- Initialize the sample values used to apply exponential smoothing.
- Process the data step by step using Exponential smoothing and Forecast.
- Display the result for apply exponential smoothing and compare it with the documented sample output.
This example of apply exponential smoothing processes the sample values in a controlled iteration. It demonstrates Exponential smoothing and Forecast and prints a deterministic result that can be checked against the sample output.
EFFICIENCY
Time and space complexity
O(n)
O(n)
DEBUGGING CHECKLIST
Common mistakes
For apply exponential smoothing, keep the data shape and value types consistent with Exponential smoothing.
Keep every dependent statement inside the correct indented Python block.
Verify the final Exponential smoothing and Forecast result against the sample before trying new data.