使用 LSTM 对销售额预测（Python代码）
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大家经常会遇到一些需要预测的场景，比如预测品牌销售额，预测产品销量。
什么是时间序列分析？ 什么是 LSTM？
单变量时间序列 多元时间序列
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dense, Dropout
from sklearn.preprocessing import MinMaxScaler
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import GridSearchCVdf=pd.read_csv("train.csv",parse_dates=["Date"],index_col=[0])
df.head()
df.tail()
df.shape
(5203,5)test_split=round(len(df)*0.20)
df_for_training=df[:-1041]
df_for_testing=df[-1041:]
print(df_for_training.shape)
print(df_for_testing.shape)
(4162, 5)
(1041, 5)scaler = MinMaxScaler(feature_range=(0,1))
df_for_training_scaled = scaler.fit_transform(df_for_training)
df_for_testing_scaled=scaler.transform(df_for_testing)
df_for_training_scaled
def createXY(dataset,n_past):
dataX = []
dataY = []
for i in range(n_past, len(dataset)):
dataX.append(dataset[i - n_past:i, 0:dataset.shape[1]])
dataY.append(dataset[i,0])
return np.array(dataX),np.array(dataY)
trainX,trainY=createXY(df_for_training_scaled,30)
testX,testY=createXY(df_for_testing_scaled,30)data_X.addend (df_for_training_scaled[i - n_past:i, 0:df_for_training.shape[1]])print("trainX Shape-- ",trainX.shape)
print("trainY Shape-- ",trainY.shape)
(4132, 30, 5)
(4132,)
print("testX Shape-- ",testX.shape)
print("testY Shape-- ",testY.shape)
(1011, 30, 5)
(1011,)print("trainX[0]-- \n",trainX[0])
print("trainY[0]-- ",trainY[0])
trainX — — →trainY
[0 : 30,0:5] → [30,0]
[1:31, 0:5] → [31,0]
[2:32,0:5] →[32,0]def build_model(optimizer):
grid_model = Sequential()
grid_model.add(LSTM(50,return_sequences=True,input_shape=(30,5)))
grid_model.add(LSTM(50))
grid_model.add(Dropout(0.2))
grid_model.add(Dense(1))
grid_model.compile(loss = 'mse',optimizer = optimizer)
return grid_modelgrid_model = KerasRegressor(build_fn=build_model,verbose=1,validation_data=(testX,testY))
parameters = {'batch_size' : [16,20],
'epochs' : [8,10],
'optimizer' : ['adam','Adadelta'] }
grid_search = GridSearchCV(estimator = grid_model,
param_grid = parameters,
cv = 2)(trainX.shape[1],trainX.shape[2]) → (30,5)grid_search = grid_search.fit(trainX,trainY)
grid_search.best_params_
{‘batch_size’: 20, ‘epochs’: 10, ‘optimizer’: ‘adam’}my_model=grid_search.best_estimator_.modelprediction=my_model.predict(testX)
print("prediction\n", prediction)
print("\nPrediction Shape-",prediction.shape)
scaler.inverse_transform(prediction)
prediction_copies_array = np.repeat(prediction,5, axis=-1)
prediction_copies_array.shape
(1011,5)pred=scaler.inverse_transform(np.reshape(prediction_copies_array,(len(prediction),5)))[:,0]original_copies_array = np.repeat(testY,5, axis=-1)
original=scaler.inverse_transform(np.reshape(original_copies_array,(len(testY),5)))[:,0]print("Pred Values-- " ,pred)
print("\nOriginal Values-- " ,original)
plt.plot(original, color = 'red', label = 'Real Stock Price')
plt.plot(pred, color = 'blue', label = 'Predicted Stock Price')
plt.title('Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Google Stock Price')
plt.legend()
plt.show()
df_30_days_past=df.iloc[-30:,:]
df_30_days_past.tail()
df_30_days_future=pd.read_csv("test.csv",parse_dates=["Date"],index_col=[0])
df_30_days_future
df_30_days_future["Open"]=0
df_30_days_future=df_30_days_future[["Open","High","Low","Close","Adj Close"]]
old_scaled_array=scaler.transform(df_30_days_past)
new_scaled_array=scaler.transform(df_30_days_future)
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=30
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=my_model.predict(data_x)
all_data.append(prediction)
full_df.iloc[i,0]=predictionnew_array=np.array(all_data)
new_array=new_array.reshape(-1,1)
prediction_copies_array = np.repeat(new_array,5, axis=-1)
y_pred_future_30_days = scaler.inverse_transform(np.reshape(prediction_copies_array,(len(new_array),5)))[:,0]
print(y_pred_future_30_days)
作者：Sksujanislam
来源：DeepHub IMBA
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