LSTM准确性低的预测

Question

我为此预测使用了LSTM模型。但是准确性很低。我该如何解决此问题？

from keras.layers import Dropout
from keras.layers import Bidirectional
model=Sequential()
model.add(LSTM(50,activation='relu',return_sequences=True,input_shape=(look_back,1)))
model.add(LSTM(50, activation='relu', return_sequences=True))
model.add(LSTM(50, activation='relu', return_sequences=True))
model.add(LSTM(50, activation='sigmoid', return_sequences=False))
model.add(Dense(50))
model.add(Dense(50))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(optimizer='adam',loss='mean_squared_error',metrics=['accuracy'])
model.optimizer.learning_rate = 0.0001

测试和火车预测图

epochs

Answer 1

您的结构看起来正确。尝试我的代码。

来自keras.models导入顺序
来自keras.layers导入LSTM，密集，辍学，双向

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
#from keras.utils import plot_model
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from keras.layers.merge import Concatenate
import matplotlib.gridspec as gridspec

import random
import scikitplot as skplot
import datetime
from datetime import date
from pandas_datareader import data as pdr

def create_dataset(dataset, look_back=3):
    dataX, dataY = [], []
    for i in range(len(dataset)-look_back-1):
        a = dataset[i:(i+look_back)]
        dataX.append(a)
        dataY.append(dataset[i + look_back])
    return np.array(dataX), np.array(dataY)

COLUMNS=['your_data_column']
dataset=df[COLUMNS]
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(np.array(dataset).reshape(-1,1))

train_size = int(len(dataset) * 0.60)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size], dataset[train_size:len(dataset)]

look_back=3
trainX=[]
testX=[]
y_train=[]
n_future = 1
features=2
timeSteps=4

model = Sequential()

model.add(Bidirectional(LSTM(units=50, return_sequences=True, 
                             input_shape=(X_train.shape[1], 1))))

model.add(LSTM(units= 50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units= 50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units= 50))
model.add(Dropout(0.2))
model.add(Dense(units = n_future))

model.compile(optimizer="adam", loss="mean_squared_error", metrics=["acc"])