深度学习算法预测（LSTM）
深度学习算法预测（LSTM）
LSTM在时间序列预测领域有着不错的表现，在进行时间序列预测时，既可以对单变量序列进行预测，也可以对多变量序列进行有效地输出。
LSTM搭建将使用tensorflow的keras模块搭建，已高度封装，可直接取用。
共封装了3个主要的函数：
- fit用于模型训练；
- evaluate用于全样本划分为训练集和验证集，验证集验证模型的表现；
- predict用于未来数据的预测，其中传入的数据是没有真实预测值的。
LSTM时间序列预测模型
其中：n_past参数控制着预测的粒度。
若n_past越小，则预测的平滑度越低，越注重于短期预测，若n_past越大，则越注重长期预测。
# 导入相关包 import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import GridSearchCV from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,LSTM,Dropout from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.wrappers.scikit_learn import KerasRegressor from sklearn.metrics import mean_absolute_error,mean_squared_error class LSTMTimePredictor: def __init__(self, df, test_ratio = 0.2, n_past=30, optimizer='adam'): ''' df:DataFrame时间序列数据; test_ratio:测试比率 n_past:预测的窗口数; optimizer:优化器; n_features:特征数; feature_names:特征名称; ''' self.df = df self.test_ratio = test_ratio self.n_past = n_past self.optimizer = optimizer self.n_features = self.df.shape[1] self.feature_names = self.df.columns def _train_test_split(self): ''' 训练测试划分; ''' test_split = round(len(self.df) * self.test_ratio) # 计算测试集中的样本数量 df_training = self.df[:-test_split] df_testing = self.df[-test_split:] # 进行最小最大归一化 scaler = MinMaxScaler() df_training_scaled = scaler.fit_transform(df_training) df_testing_scaled = scaler.transform(df_testing) # 获取训练集和测试集的样本数量 self.train_length = len(df_training_scaled) self.test_length = len(df_testing_scaled) # 获取归一化后的训练样本和测试样本 #self.df_training_scaled = df_training_scaled #self.df_training_scaled = df_testing_scaled self.scaler = scaler return df_training_scaled,df_testing_scaled def createXY(self,datasets): ''' 生成用于LSTM输入的多元数据,例如时间窗口n_past=30,则一个样本的维度为(30,5) 30代表时间窗口,5代表特征数量 ''' dataX = [] dataY = [] for i in range(self.n_past,len(datasets)): dataX.append(datasets[i - self.n_past:i,0:datasets.shape[1]]) dataY.append(datasets[i,0]) return np.array(dataX),np.array(dataY) def _build_model(self,): ''' 建立模型 ''' grid_model = Sequential() grid_model.add(LSTM(50,return_sequences=True,input_shape=(self.n_past,self.n_features))) grid_model.add(LSTM(50)) grid_model.add(Dropout(0.2)) grid_model.add(Dense(1)) grid_model.compile(loss='mse',optimizer=self.optimizer) # 封装为scikit-learn模型 return grid_model def fit(self,): df_training_scaled = self._train_test_split()[0] df_testing_scaled = self._train_test_split()[1] X_train,y_train = self.createXY(df_training_scaled) X_test,y_test = self.createXY(df_testing_scaled) grid_model = KerasRegressor(build_fn=self._build_model,verbose=1,validation_data=(X_test,y_test)) grid_model.fit(X_train,y_train) self.model = grid_model def evaluate(self,plot=True): df_testing_scaled = self._train_test_split()[1] X_test,y_test = self.createXY(df_testing_scaled) # 预测值 prediction = self.model.predict(X_test) prediction_copy_array = np.repeat(prediction,self.n_features,axis=-1) pred = self.scaler.inverse_transform(np.reshape(prediction_copy_array,(len(prediction),self.n_features)))[:,0] # 实际值 original_copies_array = np.repeat(y_test,self.n_features, axis=-1) original=self.scaler.inverse_transform(np.reshape(original_copies_array,(len(y_test),self.n_features)))[:,0] if plot: fig,ax = plt.subplots(figsize=(20,8)) ax.plot(original, color = 'red', label = 'Real Values') ax.plot(pred, color = 'blue', label = 'Predicted Values') ax.set_title('Time Series Prediction') ax.set_xlabel('Time') ax.set_ylabel('Values') ax.legend() plt.show() mae = mean_absolute_error(original,pred) mse = mean_squared_error(original,pred) mape = np.mean(np.abs(original - pred)/original) print("MSE is {},MAE is {}, MAPE is {}".format(mse,mae,mape)) return pred def predict(self,df_unknown): df_days_past=self.df.iloc[-self.n_past:,:] df_unknown[self.feature_names[0]] = 0 df_unknown = df_unknown[self.feature_names] old_scaled_array = self.scaler.transform(df_days_past) new_scaled_array = self.scaler.transform(df_unknown) new_scaled_df = pd.DataFrame(new_scaled_array) new_scaled_df.iloc[:,0] = np.nan full_df = pd.concat([pd.DataFrame(old_scaled_array),new_scaled_df]).reset_index().drop(["index"],axis=1) full_df_scaled_array = full_df.values all_data = [] time_step = self.n_past for i in range(time_step,len(full_df_scaled_array)): data_x=[] data_x.append(full_df_scaled_array[i-time_step :i , 0:full_df_scaled_array.shape[1]]) data_x=np.array(data_x) prediction=self.model.predict(data_x) all_data.append(prediction) full_df.iloc[i,0]=prediction new_array=np.array(all_data) new_array=new_array.reshape(-1,1) prediction_copies_array = np.repeat(new_array,self.n_features, axis=-1) y_pred_future_days = self.scaler.inverse_transform(np.reshape(prediction_copies_array,(len(new_array),self.n_features)))[:,0] return y_pred_future_days
多数据验证
可将以上的模型导出为py文件，重命名为LSTMTime
单变量数据集 daily-min-temperatures
import numpy as np import pandas as pd from LSTMTime import LSTMTimePredictor # 验证比例设置为0.1, 时间预测窗口设置为30 df = pd.read_csv('./daily-min-temperatures.csv') df = df.set_index('Date') lstm = LSTMTimePredictor(df,test_ratio=0.1,n_past=30) lstm.fit() lstm.evaluate()
单变量数据集 monthly-sunspots
df1 = pd.read_csv('./monthly-sunspots.csv') df1 = df1.set_index('Month') lstm = LSTMTimePredictor(df1,test_ratio=0.1,n_past=30) lstm.fit() lstm.evaluate()
多变量数据集 股票数据
df3 = pd.read_csv('./train.csv') df3 = df3.set_index('Date') df3_test = pd.read_csv('./test.csv') df3_test = df3_test.set_index('Date') lstm = LSTMTimePredictor(df3,test_ratio=0.2,n_past=25) lstm.fit() lstm.evaluate()
# 对未知开盘价进行预测
lstm.predict(df3_test)不论数据集是单变量还是多变量，抑或是单变量数据集进行特征工程后处理为多变量数据，在数据处理完毕后可直接喂入该工具中，可对不同业务场景下的时间序列预测问题进行大致的预测。如果数据复杂，可对原始模型进行结构上的优化以及调参。
================未验证=========================
