[Time Series Forecasting] Sliding Window Dataset

class SlidingWindowDataset(Dataset):
    def __init__(self, train_X, train_y, input_window, output_window, stride=1):
        self.train_X = train_X
        self.train_y = train_y
        self.input_window = input_window
        self.output_window = output_window
        self.stride = stride

        self.variables = train_X.columns # train_X에도 target이 있어야 함.
        self.target = self.train_y
        self.num_samples = max((len(self.target) - input_window - output_window) // stride + 1, 0)

    def create_features(self, df, idx):
        return np.stack([df[var].values[idx * self.stride:idx * self.stride + self.input_window] for var in self.variables], axis=-1)

    def create_target(self, target, idx):
        return target[idx * self.stride + self.input_window : idx * self.stride + self.input_window + self.output_window]

    def __getitem__(self, idx):
        X = self.create_features(self.train_X, idx)
        Y = self.create_target(self.target, idx)

        X_tensor = torch.tensor(X, dtype=torch.float32)
        Y_tensor = torch.tensor(Y, dtype=torch.float32)

        return X_tensor, Y_tensor

    def __len__(self):
        return self.num_samples