| import numpy as np | |
| from sklearn.preprocessing import MinMaxScaler | |
| from keras.models import Sequential | |
| from keras.layers import LSTM, Dense | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| class Model: | |
| def __init__(self, data): | |
| self.data = data | |
| self.scaler = MinMaxScaler(feature_range=(0, 1)) | |
| self.model = None | |
| def prepare_data(self, look_back=1): | |
| scaled_data = self.scaler.fit_transform(self.data['Close'].values.reshape(-1, 1)) | |
| def create_dataset(dataset): | |
| X, Y = [], [] | |
| for i in range(len(dataset) - look_back): | |
| a = dataset[i:(i + look_back), 0] | |
| X.append(a) | |
| Y.append(dataset[i + look_back, 0]) | |
| return np.array(X), np.array(Y) | |
| X, Y = create_dataset(scaled_data) | |
| X = np.reshape(X, (X.shape[0], 1, X.shape[1])) | |
| return X, Y | |
| def train_lstm(self, epochs=5, batch_size=1): | |
| X, Y = self.prepare_data() | |
| self.model = Sequential() | |
| self.model.add(LSTM(50, input_shape=(1, 1))) | |
| self.model.add(Dense(1)) | |
| self.model.compile(loss='mean_squared_error', optimizer='adam') | |
| self.model.fit(X, Y, epochs=epochs, batch_size=batch_size, verbose=0) | |
| def make_predictions(self): | |
| X, _ = self.prepare_data() | |
| predictions = self.model.predict(X) | |
| predictions = self.scaler.inverse_transform(predictions) | |
| return predictions | |
| def forecast_future(self, days=5): | |
| last_value = self.data['Close'].values[-1:].reshape(-1, 1) | |
| last_scaled = self.scaler.transform(last_value) | |
| future_predictions = [] | |
| for _ in range(days): | |
| prediction = self.model.predict(last_scaled.reshape(1, 1, 1))[0] | |
| future_predictions.append(prediction) | |
| last_scaled = prediction | |
| future_predictions = self.scaler.inverse_transform(future_predictions) | |
| return future_predictions |