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# ------------------------------------------------- # 3. Prepare supervised learning windows # ------------------------------------------------- def create_dataset(series, look_back=12): X, y = [], [] for i in range(len(series)-look_back): X.append(series[i:i+look_back]) y.append(series[i+look_back]) return tf.constant(X, dtype=tf.float32), tf.constant(y, dtype=tf.float32)

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# ------------------------------------------------- # 6. Real‑time inference (example) # ------------------------------------------------- def predict_next(current_window): """current_window: np.array shape (look_back, 1) already scaled""" pred_scaled = model.predict(tf.expand_dims(current_window, axis