如何使用LSTM模型进行多步预测？ [英] How to use the LSTM model for multi-step forecasting?

问题描述

我用LSTM开发了一个时间序列模型。我不能用它来预测未来几天的股价。我想用它来预测明年的股票价格，然后画出来。如何用它来预测未来(明年)的股价？

df=pd.read_csv('foolad.csv')
df=df.set_index(pd.DatetimeIndex(df['Date'].values))

data=df.filter(['Close'])
dataset=data.values

training_data_len=math.ceil(len(dataset)*0.8)
scaler=MinMaxScaler(feature_range=(0,1))
scaled_data=scaler.fit_transform(dataset)
scaled_data

training_data=scaled_data[0:training_data_len , :]

xtrain=[]
ytrain=[]
n = 60

for i in range(n,len(training_data)):
    xtrain.append(training_data[i-n:i , 0])
    ytrain.append(training_data[i,0])

xtrain , ytrain = np.array(xtrain) , np.array(ytrain)
xtrain=np.reshape(xtrain , (xtrain.shape[0],xtrain.shape[1],1))
xtrain.shape

model=Sequential()
model.add(LSTM(50,return_sequences=True,input_shape=(xtrain.shape[1],1)))
model.add(LSTM(50,return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))

model.compile(loss='mean_squared_error',optimizer='adam')

model.fit(xtrain,ytrain,epochs=1,batch_size=1)

test_data=scaled_data[training_data_len - n : , :]
xtest=[]
ytest=dataset[training_data_len : , :]
for i in range(n , len(test_data)):
    xtest.append(test_data[i-n : i , 0])

xtest=np.array(xtest)
xtest=np.reshape(xtest , (xtest.shape[0],xtest.shape[1],1))

prediction=model.predict(xtest)
prediction=scaler.inverse_transform(prediction)

#for future 360 days what can I do?....

推荐答案

一种方法是将预测作为输入反馈给模型：在每个步骤中，通过删除最旧的值并添加最新的预测作为最新的值来更新输入序列。这在下面进行了示意性说明，其中n是输入序列的长度，T是时间序列的长度。

下面的代码显示如何为您的LSTM模型实现此方法并绘制结果。

import numpy as np
import pandas as pd
import yfinance as yf
import tensorflow as tf
from tensorflow.keras.layers import Dense, LSTM
from tensorflow.keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler

# download the data
df = yf.download(tickers=['^IXIC'], period='5y')
y = df['Close'].fillna(method='ffill').values.reshape(- 1, 1)

# scale the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaler = scaler.fit(y)
y = scaler.transform(y)

# generate the training sequences
n_forecast = 1 
n_lookback = 60

X = []
Y = []

for i in range(n_lookback, len(y) - n_forecast + 1):
    X.append(y[i - n_lookback: i])
    Y.append(y[i: i + n_forecast])

X = np.array(X)
Y = np.array(Y)

# train the model
tf.random.set_seed(0)

model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(X.shape[1], X.shape[2])))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))

model.compile(loss='mse', optimizer='adam')
model.fit(X, Y, epochs=100, batch_size=128, validation_split=0.2, verbose=0)

# generate the multi-step forecasts
n_future = 365
y_future = []

x_pred = X[-1:, :, :]  # last observed input sequence
y_pred = Y[-1]         # last observed target value

for i in range(n_future):

    # feed the last forecast back to the model as an input
    x_pred = np.append(x_pred[:, 1:, :], y_pred.reshape(1, 1, 1), axis=1)

    # generate the next forecast
    y_pred = model.predict(x_pred)

    # save the forecast
    y_future.append(y_pred.flatten()[0])

# transform the forecasts back to the original scale
y_future = np.array(y_future).reshape(-1, 1)
y_future = scaler.inverse_transform(y_future)

# organize the results in a data frame
df_past = df[['Close']].reset_index()
df_past.rename(columns={'index': 'Date'}, inplace=True)
df_past['Date'] = pd.to_datetime(df_past['Date'])
df_past['Forecast'] = np.nan

df_future = pd.DataFrame(columns=['Date', 'Close', 'Forecast'])
df_future['Date'] = pd.date_range(start=df_past['Date'].iloc[-1] + pd.Timedelta(days=1), periods=n_future)
df_future['Forecast'] = y_future.flatten()
df_future['Close'] = np.nan

results = df_past.append(df_future).set_index('Date')

# plot the results
results.plot(title='NASDAQ')

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