init

forecasting_model/data_processing.py

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+import numpy as np
+import pandas as pd
+import torch
+from torch.utils.data import Dataset, DataLoader
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+from typing import Tuple, Generator, List, Optional
+from utils.config_model import DataConfig, FeatureConfig, TrainingConfig, EvaluationConfig
+
+# --- Data Loading ---
+def load_raw_data(config: DataConfig) -> pd.DataFrame:
+    """
+    Load and preprocess raw data from CSV.
+    """
+    # TODO: Implement CSV loading and datetime parsing
+    pass
+
+# --- Feature Engineering ---
+def engineer_features(df: pd.DataFrame, target_col: str, feature_config: FeatureConfig) -> pd.DataFrame:
+    """
+    Create features from the target column and datetime index.
+    """
+    # TODO: Implement feature engineering (lags, rolling stats, time features, wavelets)
+    pass
+
+# --- Cross Validation ---
+class TimeSeriesCrossValidationSplitter:
+    def __init__(self, config: CrossValidationConfig, n_samples: int):
+        self.config = config
+        self.n_samples = n_samples
+
+    def split(self) -> Generator[Tuple[np.ndarray, np.ndarray, np.ndarray], None, None]:
+        """
+        Generate train/val/test splits using expanding window approach.
+        """
+        # TODO: Implement expanding window CV splitter
+        pass
+
+# --- Dataset Class ---
+class TimeSeriesDataset(Dataset):
+    def __init__(self, data_array: np.ndarray, sequence_length: int, forecast_horizon: int):
+        self.data = data_array
+        self.sequence_length = sequence_length
+        self.forecast_horizon = forecast_horizon
+
+    def __len__(self) -> int:
+        # TODO: Implement length calculation
+        pass
+
+    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
+        # TODO: Implement sequence extraction
+        pass
+
+# --- Data Preparation ---
+def prepare_fold_data_and_loaders(
+    full_df: pd.DataFrame,
+    train_idx: np.ndarray,
+    val_idx: np.ndarray,
+    test_idx: np.ndarray,
+    feature_config: FeatureConfig,
+    train_config: TrainingConfig,
+    eval_config: EvaluationConfig
+) -> Tuple[DataLoader, DataLoader, DataLoader, object, int]:
+    """
+    Prepare data loaders for a single fold.
+    """
+    # TODO: Implement data preparation pipeline
+    pass