Implement a **difference transformation** for a time series, which converts raw values into changes between consecutive time steps. The first-order difference is defined as:
$$
\Delta x_t = x_t - x_{t-1}
$$

## Requirements

Implement the function
```python
import numpy as np

def difference_transform(series, order=1):
    \"""
    Computes the differenced version of a 1D time series.

    Args:
        series (np.ndarray): Original time series values in time order.
        order (int): The differencing order (1 = first difference, 2 = second difference, etc.).

    Returns:
        np.ndarray: The differenced series after applying `order` differencing steps.
    \"""
    # Your solution below
```

**Rules:**
- Apply differencing `order` times (e.g., order=2 means difference the series, then difference the result).
- Return a **NumPy array**.
- Do not use any prebuilt differencing utilities (e.g., pandas `.diff()` or `np.diff`). Use array slicing.
- Keep the output length consistent with differencing (each differencing step reduces length by 1).
- Use only NumPy and built-in libraries.

## Example

```python
series = np.array([10.0, 13.0, 12.0, 20.0])
difference_transform(series, order=1)
```

Output:
```python
array([ 3., -1.,  8.])
```

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Difference transformation

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1244. Difference transformation

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Similar Questions

Argument	Type
order	int
series	np.ndarray