coreforecast_grouped_array_differencing_and_scaling_quickstart.py

1import numpy as np
2from coreforecast.grouped_array import GroupedArray
3from coreforecast.scalers import LocalStandardScaler
4
5# Create sample data
6data = np.random.randn(100).astype(np.float32)
7indptr = np.array([0, 50, 100], dtype=np.int32)
8ga = GroupedArray(data, indptr)
9
10# Scaler example
11scaler = LocalStandardScaler()
12# Compute statistics (mean and std) for each group
13stats = scaler.fit(ga)
14# Transform the data (z-score normalization)
15transformed = scaler.transform(ga)
16# Inverse transform to get original scale
17restored = scaler.inverse_transform(ga)
18
19# Differencing example
20from coreforecast.diff import diff
21
22# Apply first-order differencing with lag 1
23d_data = diff(data, indptr, diffs=[1])
24print(f"Original shape: {data.shape}")
25print(f"Differenced shape: {d_data.shape}")