{"id":1398,"date":"2024-09-06T07:32:00","date_gmt":"2024-09-05T23:32:00","guid":{"rendered":"https:\/\/blog.laoyulaoyu.top\/?p=1398"},"modified":"2024-09-02T19:43:46","modified_gmt":"2024-09-02T11:43:46","slug":"%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e3%80%81%e7%94%9f%e6%88%90%e5%bc%8fai%e5%92%8c%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97%e6%a8%a1%e5%9e%8b%ef%bc%88%e5%90%ab%e4%bb%a3","status":"publish","type":"post","link":"https:\/\/www.laoyulaoyu.com\/index.php\/2024\/09\/06\/%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e3%80%81%e7%94%9f%e6%88%90%e5%bc%8fai%e5%92%8c%e6%b7%b1%e5%ba%a6%e5%ad%a6%e4%b9%a0%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97%e6%a8%a1%e5%9e%8b%ef%bc%88%e5%90%ab%e4%bb%a3\/","title":{"rendered":"\u673a\u5668\u5b66\u4e60\u3001\u751f\u6210\u5f0fAI\u548c\u6df1\u5ea6\u5b66\u4e60\u65f6\u95f4\u5e8f\u5217\u6a21\u578b\uff08\u542b\u4ee3\u7801\uff09"},"content":{"rendered":"\n

\u4f5c\u8005\uff1a\u8001\u4f59\u635e\u9c7c<\/a><\/p>\n\n\n\n

\u539f\u521b\u4e0d\u6613\uff0c\u8f6c\u8f7d\u8bf7\u6807\u660e\u51fa\u5904\u53ca\u539f\u4f5c\u8005\u3002<\/mark><\/strong><\/p>\n\n\n\n

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\u5199\u5728\u524d\u9762\u7684\u8bdd\uff1a<\/strong>\u672c\u6587\u8f6c\u81ea\u4e00\u7bc7\u8bba\u6587\uff0c\u4e3b\u8981\u8ba8\u8bba\u4e86\u5728\u4e0d\u540c\u884c\u4e1a\u4e2d\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b<\/mark>\u7684\u91cd\u8981\u6027\uff0c\u4ee5\u53ca\u5982\u4f55\u5229\u7528\u673a\u5668\u5b66\u4e60\u3001\u751f\u6210\u5f0f\u4eba\u5de5\u667a\u80fd\uff08Generative AI\uff09\u548c\u6df1\u5ea6\u5b66\u4e60\u6765\u63d0\u9ad8\u9884\u6d4b\u7684\u51c6\u786e\u6027\u3002<\/mark>\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u662f\u6309\u7279\u5b9a\u65f6\u95f4\u95f4\u9694\u6536\u96c6\u6216\u8bb0\u5f55\u7684\u6570\u636e\u70b9\u5e8f\u5217\uff0c\u4f8b\u5982\u80a1\u7968\u4ef7\u683c\u3001\u5929\u6c14\u6570\u636e\u3001\u9500\u552e\u6570\u5b57\u548c\u4f20\u611f\u5668\u8bfb\u6570\u3002\u9884\u6d4b\u672a\u6765\u503c\u7684\u80fd\u529b\u53ef\u4ee5\u663e\u8457\u6539\u8fdb\u51b3\u7b56\u8fc7\u7a0b\u548c\u8fd0\u8425\u6548\u7387\u3002<\/pre>\n<\/blockquote>\n\n\n\n
\u672c\u6587\u4ecb\u7ecd\u4e86\u5305\u62ecARIMA\u3001SARIMA\u3001Prophet\u3001XGBoost\u3001GANs\u3001WaveNet\u3001LSTM\u3001GRU\u3001Transformer\u3001Seq2Seq\u3001TCN\u548cDeepAR\u5728\u5185\u7684\u591a\u79cd\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u6a21\u578b\uff0c\u4ecb\u7ecd\u4e86\u5404\u81ea\u6a21\u578b\u7684\u7279\u70b9\u4ee5\u53ca\u5177\u4f53\u4ee3\u7801\u3002\u5982\u6211\u4eec\u9996\u5148\u4ece\u673a\u5668\u5b66\u4e60\u65b9\u6cd5<\/strong>\u5f00\u59cb\u3002<\/p>\n\n\n\n
\u4e00\u3001\u673a\u5668\u5b66\u4e60\u65b9\u6cd5<\/strong><\/h2>\n\n\n\n
<\/p>\n\n\n\n
1.1 ARIMA\uff08\u81ea\u56de\u5f52\u79ef\u5206\u6ed1\u52a8\u5e73\u5747\u6a21\u578b\uff09<\/strong><\/h3>\n\n\n\n
ARIMA\uff08\u81ea\u56de\u5f52\u79ef\u5206\u6ed1\u52a8\u5e73\u5747\u6a21\u578b\uff09\u4e00\u79cd\u7ecf\u5178\u7684\u7edf\u8ba1\u65b9\u6cd5\uff0c\u7ed3\u5408\u4e86\u81ea\u56de\u5f52\uff08AR\uff09\u3001\u5dee\u5206\uff08\u4f7f\u6570\u636e\u5e73\u7a33\uff09\u548c\u6ed1\u52a8\u5e73\u5747\uff08MA\uff09\u6a21\u578b\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
import pandas as pd<\/code>from statsmodels.tsa.arima.model import ARIMA<\/code>
<\/code># Load your time series data<\/code>time_series_data = pd.read_csv('time_series_data.csv')<\/code>time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])<\/code>time_series_data.set_index('Date', inplace=True)<\/code>
<\/code># Fit ARIMA model<\/code>model = ARIMA(time_series_data['Value'], order=(5, 1, 0))  # (p,d,q)<\/code>model_fit = model.fit()<\/code>
<\/code># Make predictions<\/code>predictions = model_fit.forecast(steps=10)<\/code>print(predictions)<\/code><\/code><\/pre>\n\n\n\n
1.2 SARIMA\uff08\u5b63\u8282\u6027ARIMA\uff09<\/strong><\/h3>\n\n\n\n
SARIMA\uff08\u5b63\u8282\u6027ARIMA\uff09\u662f\u5728ARIMA\u7684\u57fa\u7840\u4e0a\u589e\u52a0\u4e86\u5b63\u8282\u6027\u6548\u5e94\u7684\u8003\u8651\u3002<\/p>\n\n\n\n
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    \n
  • <\/li>\n<\/ul>\n\n\n\n
    import pandas as pd<\/code>import numpy as np<\/code>from statsmodels.tsa.statespace.sarimax import SARIMAX<\/code>
    <\/code># Load your time series data<\/code>time_series_data = pd.read_csv('time_series_data.csv')<\/code>time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])<\/code>time_series_data.set_index('Date', inplace=True)<\/code>
    <\/code># Fit SARIMA model<\/code>model = SARIMAX(time_series_data['Value'], order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))  # (p,d,q) (P,D,Q,s)<\/code>model_fit = model.fit(disp=False)<\/code>
    <\/code># Make predictions<\/code>predictions = model_fit.forecast(steps=10)<\/code>print(predictions)<\/code><\/code><\/pre>\n\n\n\n
    1.3 Prophet<\/strong><\/h3>\n\n\n\n
    Prophet\u7531Facebook\u5f00\u53d1\uff0c\u9002\u7528\u4e8e\u5904\u7406\u7f3a\u5931\u6570\u636e\u548c\u5f02\u5e38\u503c\uff0c\u5e76\u63d0\u4f9b\u53ef\u9760\u7684\u4e0d\u786e\u5b9a\u6027\u533a\u95f4\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    from fbprophet import Prophetimport pandas as pd\n# Load your time series datatime_series_data = pd.read_csv('time_series_data.csv')time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])time_series_data.rename(columns={'Date': 'ds', 'Value': 'y'}, inplace=True)\n# Fit Prophet modelmodel = Prophet()model.fit(time_series_data)\n# Make future dataframe and predictionsfuture = model.make_future_dataframe(periods=10)forecast = model.predict(future)print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])<\/code><\/code><\/pre>\n\n\n\n
    1.4 XGBoost<\/strong><\/h3>\n\n\n\n
    XGBoost\u662f\u4e00\u79cd\u68af\u5ea6\u589e\u5f3a\u6846\u67b6\uff0c\u901a\u8fc7\u5c06\u95ee\u9898\u8f6c\u5316\u4e3a\u76d1\u7763\u5b66\u4e60\u4efb\u52a1\u6765\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    import pandas as pdimport numpy as npfrom xgboost import XGBRegressorfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import mean_squared_error\n# Load your time series datatime_series_data = pd.read_csv('time_series_data.csv')time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])time_series_data.set_index('Date', inplace=True)\n# Prepare data for supervised learningdef create_lag_features(data, lag=1): df = data.copy() for i in range(1, lag + 1): df[f'lag_{i}'] = df['Value'].shift(i) return df.dropna()\nlag = 5data_with_lags = create_lag_features(time_series_data, lag=lag)X = data_with_lags.drop('Value', axis=1)y = data_with_lags['Value']\n# Split the data into training and testing setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n# Fit XGBoost modelmodel = XGBRegressor(objective='reg:squarederror', n_estimators=1000)model.fit(X_train, y_train)\n# Make predictionsy_pred = model.predict(X_test)mse = mean_squared_error(y_test, y_pred)print(f'Mean Squared Error: {mse}')<\/code><\/code><\/pre>\n\n\n\n
    \u4e8c\u3001\u751f\u6210\u5f0fAI\u65b9\u6cd5<\/strong><\/h2>\n\n\n\n
    <\/p>\n\n\n\n
    2.1 GANs\uff08\u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff09<\/strong><\/h3>\n\n\n\n
    GANs\uff08\u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff09\u7531\u751f\u6210\u5668\u548c\u9274\u522b\u5668\u7ec4\u6210\uff0c\u53ef\u4ee5\u751f\u6210\u5408\u7406\u7684\u672a\u6765\u5e8f\u5217\u3002<\/p>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
      \n
    • <\/li>\n<\/ul>\n\n\n\n
      import numpy as np<\/code>import pandas as pd<\/code>from tensorflow.keras.models import Sequential<\/code>from tensorflow.keras.layers import Dense, LSTM, Conv1D, MaxPooling1D, Flatten, LeakyReLU, Reshape<\/code>from tensorflow.keras.optimizers import Adam<\/code>
      <\/code># Load your time series data<\/code>time_series_data = pd.read_csv('time_series_data.csv')<\/code>time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])<\/code>time_series_data.set_index('Date', inplace=True)<\/code>
      <\/code># Prepare data for GAN<\/code>def create_dataset(dataset, time_step=1):<\/code>    X, Y = [], []<\/code>    for i in range(len(dataset)-time_step-1):<\/code>        a = dataset[i:(i+time_step), 0]<\/code>        X.append(a)<\/code>        Y.append(dataset[i + time_step, 0])<\/code>    return np.array(X), np.array(Y)<\/code>
      <\/code>time_step = 10<\/code>scaler = MinMaxScaler(feature_range=(0, 1))<\/code>scaled_data = scaler.fit_transform(time_series_data['Value'].values.reshape(-1, 1))<\/code>
      <\/code>X_train, y_train = create_dataset(scaled_data, time_step)<\/code>X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)<\/code>
      <\/code># GAN components<\/code>def build_generator():<\/code>    model = Sequential()<\/code>    model.add(Dense(100, input_dim=time_step))<\/code>    model.add(LeakyReLU(alpha=0.2))<\/code>    model.add(Dense(time_step, activation='tanh'))<\/code>    model.add(Reshape((time_step, 1)))<\/code>    return model<\/code>
      <\/code>def build_discriminator():<\/code>    model = Sequential()<\/code>    model.add(LSTM(50, input_shape=(time_step, 1)))<\/code>    model.add(Dense(1, activation='sigmoid'))<\/code>    return model<\/code>
      <\/code># Build and compile the discriminator<\/code>discriminator = build_discriminator()<\/code>discriminator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5), metrics=['accuracy'])<\/code>
      <\/code># Build the generator<\/code>generator = build_generator()<\/code>
      <\/code># The generator takes noise as input and generates data<\/code>z = Input(shape=(time_step,))<\/code>generated_data = generator(z)<\/code>
      <\/code># For the combined model, we will only train the generator<\/code>discriminator.trainable = False<\/code>
      <\/code># The discriminator takes generated data as input and determines validity<\/code>validity = discriminator(generated_data)<\/code>
      <\/code># The combined model (stacked generator and discriminator)<\/code>combined = Model(z, validity)<\/code>combined.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))<\/code>
      <\/code># Training the GAN<\/code>epochs = 10000<\/code>batch_size = 32<\/code>valid = np.ones((batch_size, 1))<\/code>fake = np.zeros((batch_size, 1))<\/code>
      <\/code>for epoch in range(epochs):<\/code>    # ---------------------<\/code>    #  Train Discriminator<\/code>    # ---------------------<\/code>
      <\/code>    # Select a random batch of real data<\/code>    idx = np.random.randint(0, X_train.shape[0], batch_size)<\/code>    real_data = X_train[idx]<\/code>
      <\/code>    # Generate a batch of fake data<\/code>    noise = np.random.normal(0, 1, (batch_size, time_step))<\/code>    gen_data = generator.predict(noise)<\/code>
      <\/code>    # Train the discriminator<\/code>    d_loss_real = discriminator.train_on_batch(real_data, valid)<\/code>    d_loss_fake = discriminator.train_on_batch(gen_data, fake)<\/code>    d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)<\/code>
      <\/code>    # ---------------------<\/code>    #  Train Generator<\/code>    # ---------------------<\/code>
      <\/code>    noise = np.random.normal(0, 1, (batch_size, time_step))<\/code>
      <\/code>    # Train the generator (to have the discriminator label samples as valid)<\/code>    g_loss = combined.train_on_batch(noise, valid)<\/code>
      <\/code>    # Print the progress<\/code>    if epoch % 1000 == 0:<\/code>        print(f\"{epoch} [D loss: {d_loss[0]} | D accuracy: {100*d_loss[1]}] [G loss: {g_loss}]\")<\/code>
      <\/code># Make predictions<\/code>noise = np.random.normal(0, 1, (1, time_step))<\/code>generated_prediction = generator.predict(noise)<\/code>generated_prediction = scaler.inverse_transform(generated_prediction)<\/code>print(generated_prediction)<\/code><\/code><\/pre>\n\n\n\n
      2.2 WaveNet<\/strong><\/h3>\n\n\n\n
      WaveNet\u7531DeepMind\u5f00\u53d1\uff0c\u6700\u521d\u7528\u4e8e\u97f3\u9891\u751f\u6210\uff0c\u4f46\u5df2\u9002\u5e94\u4e8e\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002<\/p>\n\n\n\n
      \"\"<\/figure>\n\n\n\n
        \n
      • <\/li>\n<\/ul>\n\n\n\n
        import numpy as np<\/code>import pandas as pd<\/code>import tensorflow as tf<\/code>from sklearn.preprocessing import MinMaxScaler<\/code>from tensorflow.keras.models import Model<\/code>from tensorflow.keras.layers import Input, Conv1D, Add, Activation, Multiply, Lambda, Dense, Flatten<\/code>from tensorflow.keras.optimizers import Adam<\/code>
        <\/code># Load your time series data<\/code>time_series_data = pd.read_csv('time_series_data.csv')<\/code>time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])<\/code>time_series_data.set_index('Date', inplace=True)<\/code>
        <\/code># Prepare data for WaveNet<\/code>scaler = MinMaxScaler(feature_range=(0, 1))<\/code>scaled_data = scaler.fit_transform(time_series_data['Value'].values.reshape(-1, 1))<\/code>
        <\/code>def create_dataset(dataset, time_step=1):<\/code>    X, Y = [], []<\/code>    for i in range(len(dataset)-time_step-1):<\/code>        a = dataset[i:(i+time_step), 0]<\/code>        X.append(a)<\/code>        Y.append(dataset[i + time_step, 0])<\/code>    return np.array(X), np.array(Y)<\/code>
        <\/code>time_step = 10<\/code>X, y = create_dataset(scaled_data, time_step)<\/code>X = X.reshape(X.shape[0], X.shape[1], 1)<\/code>
        <\/code># Define WaveNet model<\/code>def residual_block(x, dilation_rate):<\/code>    tanh_out = Conv1D(32, kernel_size=2, dilation_rate=dilation_rate, padding='causal', activation='tanh')(x)<\/code>    sigm_out = Conv1D(32, kernel_size=2, dilation_rate=dilation_rate, padding='causal', activation='sigmoid')(x)<\/code>    out = Multiply()([tanh_out, sigm_out])<\/code>    out = Conv1D(32, kernel_size=1, padding='same')(out)<\/code>    out = Add()([out, x])<\/code>    return out<\/code>
        <\/code>input_layer = Input(shape=(time_step, 1))<\/code>out = Conv1D(32, kernel_size=2, padding='causal', activation='tanh')(input_layer)<\/code>skip_connections = []<\/code>for i in range(10):<\/code>    out = residual_block(out, 2**i)<\/code>    skip_connections.append(out)<\/code>
        <\/code>out = Add()(skip_connections)<\/code>out = Activation('relu')(out)<\/code>out = Conv1D(1, kernel_size=1, activation='relu')(out)<\/code>out = Flatten()(out)<\/code>out = Dense(1)(out)<\/code>
        <\/code>model = Model(input_layer, out)<\/code>model.compile(optimizer=Adam(learning_rate=0.001), loss='mean_squared_error')<\/code>
        <\/code># Train the model<\/code>model.fit(X, y, epochs=10, batch_size=16)<\/code>
        <\/code># Make predictions<\/code>predictions = model.predict(X)<\/code>predictions = scaler.inverse_transform(predictions)<\/code>print(predictions)<\/code><\/code><\/pre>\n\n\n\n
        \u4e09\u3001\u6df1\u5ea6\u5b66\u4e60\u65b9\u6cd5<\/strong><\/h2>\n\n\n\n
        <\/p>\n\n\n\n
        3.1 LSTM\uff08\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc\uff09<\/strong><\/h3>\n\n\n\n
        LSTM\uff08\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc\uff09\u662fLSTM\uff08\u957f\u77ed\u671f\u8bb0\u5fc6\u7f51\u7edc\uff09\u3002<\/p>\n\n\n\n
        \"\"<\/figure>\n\n\n\n
          \n
        • <\/li>\n<\/ul>\n\n\n\n
          import numpy as np<\/code>import pandas as pd<\/code>from tensorflow.keras.models import Sequential<\/code>from tensorflow.keras.layers import LSTM, Dense<\/code>from sklearn.preprocessing import MinMaxScaler<\/code>
          <\/code># Load your time series data<\/code>time_series_data = pd.read_csv('time_series_data.csv')<\/code>time_series_data['Date'] = pd.to_datetime(time_series_data['Date'])<\/code>time_series_data.set_index('Date', inplace=True)<\/code>
          <\/code># Prepare data for LSTM<\/code>scaler = MinMaxScaler(feature_range=(0, 1))<\/code>scaled_data = scaler.fit_transform(time_series_data['Value'].values.reshape(-1, 1))<\/code>
          <\/code>train_size = int(len(scaled_data) * 0.8)<\/code>train_data = scaled_data[:train_size]<\/code>test_data = scaled_data[train_size:]<\/code>
          <\/code>def create_dataset(dataset, time_step=1):<\/code>    X, Y = [], []<\/code>    for i in range(len(dataset)-time_step-1):<\/code>        a = dataset[i:(i+time_step), 0]<\/code>        X.append(a)<\/code>        Y.append(dataset[i + time_step, 0])<\/code>    return np.array(X), np.array(Y)<\/code>
          <\/code>time_step = 10<\/code>X_train, y_train = create_dataset(train_data, time_step)<\/code>X_test, y_test = create_dataset(test_data, time_step)<\/code>
          <\/code>X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)<\/code>X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)<\/code>
          <\/code># Build LSTM model<\/code>model = Sequential()<\/code>model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))<\/code>model.add(LSTM(50, return_sequences=False))<\/code>model.add(Dense(25))<\/code>model.add(Dense(1))<\/code>
          <\/code>model.compile(optimizer='adam', loss='mean_squared_error')<\/code>model.fit(X_train, y_train, batch_size=1, epochs=1)<\/code>
          <\/code># Make predictions<\/code>train_predict = model.predict(X_train)<\/code>test_predict = model.predict(X_test)<\/code>
          <\/code>train_predict = scaler.inverse_transform(train_predict)<\/code>test_predict = scaler.inverse_transform(test_predict)<\/code>print(test_predict)<\/code><\/code><\/pre>\n\n\n\n
          3.2 GRU\uff08\u95e8\u63a7\u5faa\u73af\u5355\u5143\uff09<\/strong><\/h3>\n\n\n\n
          GRU\uff08\u95e8\u63a7\u5faa\u73af\u5355\u5143\uff09\u5c5e\u4e8eLSTM\u7684\u53d8\u4f53\uff0c\u66f4\u7b80\u5355\uff0c\u901a\u5e38\u5728\u65f6\u95f4\u5e8f\u5217\u4efb\u52a1\u4e2d\u8868\u73b0\u540c\u6837\u51fa\u8272\u3002<\/p>\n\n\n\n
          \"\"<\/figure>\n\n\n\n