Find Strictly Decreasing Forecast Windows for Equatorial Cities

Question

The candidate is asked to develop a solution for identifying strictly decreasing forecast windows for equatorial cities based on given temperature data.

Codemia · Accepted Answer

To solve the problem of finding strictly decreasing forecast windows for equatorial cities, we need to analyze a series of temperature readings. A strictly decreasing window is defined as a sequence of temperatures where each subsequent temperature is less than the previous one. Our goal is to identify all such windows in the provided dataset.

We can approach this problem by iterating through the temperature data and maintaining a count of the current strictly decreasing sequence. Whenever we encounter a temperature that is not less than the previous one, we can check if we have a valid window and store it. This method ensures we efficiently traverse the data with minimal additional storage.

Here is a Python implementation of the proposed solution:

```python
def find_decreasing_windows(temperatures):
    windows = []
    current_window = []

for i in range(len(temperatures)):
        if i == 0 or temperatures[i] < temperatures[i - 1]:
            current_window.append(temperatures[i])
        else:
            if len(current_window) > 1:
                windows.append(current_window)
            current_window = [temperatures[i]]

if len(current_window) > 1:
        windows.append(current_window)

return windows
```
This code iterates through the list of temperatures and builds windows of strictly decreasing values.

The time complexity of this algorithm is O(n), where n is the number of temperature readings, since we make a single pass through the data. The space complexity is O(k), where k is the number of strictly decreasing windows stored, which is efficient given the constraints.

It is important to consider edge cases such as:
1. An empty list of temperatures should return an empty result.
2. A list with all temperatures being the same should also return an empty result.
3. A list with only one temperature should return an empty result as well.
Handling these cases ensures robustness in our solution.

Find Strictly Decreasing Forecast Windows for Equatorial Cities

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