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import numpy as np import pandas as pd import matplotlib.pyplot as plt import plotly.express as px from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_percentage_error import tensorflow as tf from keras import Model from keras.layers import Input, Dense, Dropout, LSTM df = pd.read_csv('Gold Price (2013-2023).csv' ) df
df.info() <class 'pandas.core.frame.DataFrame'> RangeIndex: 2583 entries, 0 to 2582 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date 2583 non-null object 1 Price 2583 non-null object 2 Open 2583 non-null object 3 High 2583 non-null object 4 Low 2583 non-null object 5 Vol. 2578 non-null object 6 Change % 2583 non-null object dtypes: object(7) memory usage: 141.4+ KB df.drop(['Vol.', 'Change %'], axis=1, inplace=True) df
# Convert the 'Date' column to datetime df['Date'] = pd.to_datetime(df['Date']) # Sort the DataFrame by the 'Date' column in ascending order df.sort_values(by='Date', ascending=True, inplace=True) # Reset the index of the DataFrame df.reset_index(drop=True, inplace=True) numCols = df.columns.drop('Date') df[numCols] = df[numCols].replace({',': ''}, regex=True) df[numCols] = df[numCols].astype('float64') df.head() df.duplicated().sum() df.isnull().sum() Date 0 Price 0 Open 0 High 0 Low 0 dtype: int64 import plotly.express as px fig = px.line(y=df['Price'], x=df['Date']) fig.update_traces(line_color='black') fig.update_layout( xaxis_title='Date', yaxis_title='Price', title={ 'text': 'Gold Price Data', 'y': 0.95, 'x': 0.5, 'xanchor': 'center', 'yanchor': 'top' }, plot_bgcolor='rgba(255,223,0,0.9)' ) fig.show() test_size = df[df.Date.dt.year == 2022].shape[0] print(test_size)260
import matplotlib.pyplot as plt plt.figure(figsize=(15, 6), dpi=150) plt.rcParams['axes.facecolor'] = 'cyan' plt.rc('axes', edgecolor='white') plt.plot(df.Date[:-test_size], df.Price[:-test_size], color='black', lw=2) plt.plot(df.Date[-test_size:], df.Price[-test_size:], color='red', lw=2) plt.title('Gold Price Train and Test', fontsize=15) plt.xlabel('Date', fontsize=12) plt.ylabel('Price', fontsize=12) plt.legend(['Train Set', 'Test Set'], loc='upper left', prop={'size': 15}) plt.grid(color='white') plt.show()
scaler = MinMaxScaler() scaler.fit(df.Price.values.reshape(-1, 1)) MinMaxScaler() window_size = 60 train_data = df.Price[:-test_size] train_data = scaler.fit_transform(train_data.values.reshape(-1, 1)) window_size = 60 X_train = [] y_train = [] for i in range(window_size, len(train_data)): X_train.append(train_data[i-window_size:i, 0]) y_train.append(train_data[i, 0]) test_data = df.Price[-test_size-window_size:] test_data = scaler.transform(test_data.values.reshape(-1, 1)) X_test = [] y_test = [] for i in range(window_size, len(test_data)): X_test.append(test_data[i-window_size:i, 0]) y_test.append(test_data[i, 0]) X_train = np.array(X_train) X_test = np.array(X_test) y_train = np.array(y_train) y_test = np.array(y_test) X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) y_train = np.reshape(y_train, (-1, 1)) y_test = np.reshape(y_test, (-1, 1)) print('X_train shape:', X_train.shape) print('y_train shape:', y_train.shape) print('X_test shape:', X_test.shape) print('y_test shape:', y_test.shape) X_train shape: (2263, 60, 1) y_train shape: (2263, 1) X_test shape: (260, 60, 1) y_test shape: (260, 1) import tensorflow as tf def define_model(): input1 = Input(shape=(window_size, 1)) x = tf.keras.layers.LSTM(units=64, return_sequences=True)(input1) x = tf.keras.layers.Dropout(0.2)(x) x = tf.keras.layers.LSTM(units=64, return_sequences=True)(x) x = tf.keras.layers.Dropout(0.2)(x) x = tf.keras.layers.LSTM(units=64)(x) x = tf.keras.layers.Dropout(0.2)(x) x = tf.keras.layers.Dense(32, activation='softmax')(x) dnn_output = tf.keras.layers.Dense(1)(x) model = tf.keras.models.Model(inputs=input1, outputs=dnn_output) # Import and use the Nadam optimizer model.compile(loss='mean_squared_error', optimizer=tf.keras.optimizers.Nadam()) model.summary() return model model = define_model() history = model.fit(X_train, y_train, epochs=150, batch_size=32, validation_split=0.1, verbose=1) Model: "model_3" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_4 (InputLayer) [(None, 60, 1)] 0 lstm_9 (LSTM) (None, 60, 64) 16896 dropout_9 (Dropout) (None, 60, 64) 0 lstm_10 (LSTM) (None, 60, 64) 33024 dropout_10 (Dropout) (None, 60, 64) 0 lstm_11 (LSTM) (None, 64) 33024 dropout_11 (Dropout) (None, 64) 0 dense_6 (Dense) (None, 32) 2080 dense_7 (Dense) (None, 1) 33 ================================================================= Total params: 85057 (332.25 KB) Trainable params: 85057 (332.25 KB) Non-trainable params: 0 (0.00 Byte) _________________________________________________________________ result = model.evaluate(X_test, y_test) y_pred = model.predict(X_test) MAPE = mean_absolute_percentage_error(y_test, y_pred) Accuracy = 1 - MAPE print('Test Loss:', result) print('Test MAPE:', MAPE) print('Test Accuracy:', Accuracy) Test Loss: 0.0008509838371537626 Test MAPE: 0.0319030650799213 Test Accuracy: 0.9680969349200788 y_test_true = scaler.inverse_transform(y_test.reshape(-1, 1)).flatten() y_test_pred = scaler.inverse_transform(y_pred.reshape(-1, 1)).flatten() plt.figure(figsize=(15, 6), dpi=150) plt.rcParams['axes.facecolor'] = 'cyan' plt.rc('axes', edgecolor='white') plt.plot(df.Date[:-test_size], df.Price[:-test_size], color='black', lw=2) plt.plot(df.Date[-test_size:], df.Price[-test_size:], color='red', lw=2) plt.title('Gold Price Train and Test', fontsize=15) plt.xlabel('Date', fontsize=12) plt.ylabel('Price', fontsize=12) plt.legend(['Train Set', 'Test Set'], loc='upper left', prop={'size': 15}) plt.grid(color='white') plt.show()
知乎学术咨询：https://www.zhihu.com/consult/people/792359672131756032?isMe=1担任《Mechanical System and Signal Processing》审稿专家，担任《中国电机工程学报》，《控制与决策》等EI期刊审稿专家，擅长领域：现代信号处理，机器学习，深度学习，数字孪生，时间序列分析，设备缺陷检测、设备异常检测、设备智能故障诊断与健康管理PHM等。
