https://xainome-blog.beeskynohito.workers.dev/tags/lstm/Recent content in Lstm on Xainome BlogHugojaWed, 24 Jul 2024 00:00:00 +0000https://xainome-blog.beeskynohito.workers.dev/posts/2024/07/stock-price-prediction-part-9/Wed, 24 Jul 2024 00:00:00 +0000https://xainome-blog.beeskynohito.workers.dev/posts/2024/07/stock-price-prediction-part-9/<h2 id="前回のあらすじ">前回のあらすじ</h2> <p>確率的勾配降下法を使って予測をしてみました。</p> <p>うまくいかなかったので、別の方法を試すことにしました。</p> <h2 id="lstm">LSTM</h2> <p>時系列分析の代表モデルと言えばLSTMだと思います。</p> <p>というわけで前回とはプログラムを分けて実装しました。</p> <pre tabindex="0"><code>import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM, Input import matplotlib.pyplot as plt import glob dm_fol = &#39;./datamart/&#39; stock_list = glob.glob(dm_fol + &#34;*.parquet&#34;) # データの読み込みと前処理 def preprocess_data(df, time_step=60): df[&#39;Close&#39;] = df[&#39;Close&#39;].astype(float) data = df[[&#39;Close&#39;]].values # データのスケーリング scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(data) # LSTM用のデータセット作成 X, Y = [], [] for i in range(time_step, len(scaled_data)): X.append(scaled_data[i-time_step:i, 0]) Y.append(scaled_data[i, 0]) X, Y = np.array(X), np.array(Y) X = X.reshape(X.shape[0], X.shape[1], 1) return X, Y, scaler # LSTMモデルの構築 def create_lstm_model(input_shape): model = Sequential() model.add(Input(shape=input_shape)) # Inputオブジェクトを使用して入力形状を指定 model.add(LSTM(50, return_sequences=True)) # model.add(LSTM(50, return_sequences=True, input_shape=input_shape)) model.add(LSTM(50, return_sequences=False)) model.add(Dense(25)) model.add(Dense(1)) model.compile(optimizer=&#39;adam&#39;, loss=&#39;mean_squared_error&#39;) return model # モデルの評価とプロット def evaluate_model(df, model, scaler, X_train, y_train, X_test, y_test, time_step, sticker_name): y_train_pred = model.predict(X_train) y_test_pred = model.predict(X_test) y_train_pred = scaler.inverse_transform(y_train_pred) y_test_pred = scaler.inverse_transform(y_test_pred) y_train = scaler.inverse_transform([y_train]) y_test = scaler.inverse_transform([y_test]) train_mse = np.mean((y_train_pred - y_train[0]) ** 2) test_mse = np.mean((y_test_pred - y_test[0]) ** 2) print(f&#34;Train Mean Squared Error: {train_mse}&#34;) print(f&#34;Test Mean Squared Error: {test_mse}&#34;) # プロット df.set_index(&#39;Date&#39;, inplace=True) train_dates = df.iloc[time_step:len(y_train[0])+time_step].index test_dates = df.iloc[len(y_train[0])+time_step-1:-1].index plt.figure(figsize=(14, 7)) # plt.plot(train_dates, y_train[0], label=&#39;Train Actual&#39;, color=&#39;black&#39;) plt.plot(test_dates, y_test[0], label=&#39;Test Actual&#39;, color=&#39;red&#39;) plt.plot(test_dates, y_test_pred, label=&#39;Test Predicted&#39;, color=&#39;blue&#39;) plt.title(sticker_name+&#39;: Actual vs Predicted Stock Prices&#39;) plt.xlabel(&#39;Date&#39;) plt.ylabel(&#39;Close&#39;) plt.legend() plt.show() # 次の日の予測 def predict_next_day(model, scaler, df, time_step=60): data = df[[&#39;Close&#39;]].values last_data = data[-time_step:] last_data_scaled = scaler.transform(last_data) X_input = last_data_scaled.reshape(1, time_step, 1) next_day_prediction = model.predict(X_input) next_day_prediction = scaler.inverse_transform(next_day_prediction) print(f&#34;Next Day Prediction: {next_day_prediction[0][0]}&#34;) # データの読み込み for count, stock_data in enumerate(stock_list): df = pd.read_parquet(stock_data) sticker_name = stock_data.split(&#39;\\&#39;)[1].replace(&#39;.parquet&#39;, &#39;&#39;) # データの前処理 time_step = 60 X, Y, scaler = preprocess_data(df, time_step) # データの分割 train_size = int(len(X) * 0.8) X_train, X_test = X[:train_size], X[train_size:] y_train, y_test = Y[:train_size], Y[train_size:] # モデルの構築と訓練 model = create_lstm_model((X_train.shape[1], 1)) model.fit(X_train, y_train, batch_size=1, epochs=1) # モデルの評価 evaluate_model(df, model, scaler, X_train, y_train, X_test, y_test, time_step, sticker_name) # 次の日の予測 predict_next_day(model, scaler, df, time_step) </code></pre><h2 id="コード確認">コード確認</h2> <pre tabindex="0"><code> # LSTM用のデータセット作成 X, Y = [], [] for i in range(time_step, len(scaled_data)): X.append(scaled_data[i-time_step:i, 0]) Y.append(scaled_data[i, 0]) X, Y = np.array(X), np.array(Y) X = X.reshape(X.shape[0], X.shape[1], 1) </code></pre><p>LSTM用のデータセットは60日分のデータを学習として使い、次の日を出力として使用するという特徴があります。</p>