## Time Series Decomposition
Time series decomposition is a statistical technique used to break down a time series into its trend, seasonality, and residuals components. This can help identify patterns and trends in the data.
**Example:** Suppose we have a time series representing the number of sales over the past year. We can use time series decomposition to identify the trend, seasonality, and residuals components of the data.
**Constraints:** The input to the function will be a numpy array representing the time series data. The function should return the trend, seasonality, and residuals components.
Test Cases
Test Case 1
Input:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]Expected:
['1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0', '1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0', '0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0']Test Case 2
Input:
[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]Expected:
['10.0 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0', '0.0 -1.0 -2.0 -3.0 -4.0 -5.0 -6.0 -7.0 -8.0 -9.0', '10.0 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0']+ 3 hidden test cases