簡介和問題：我對 Keras 和深度學習有些陌生，我正在嘗試使用 LSTM 預測特斯拉的股價。我絕對是一個機器學習/深度學習的初學者，所以我希望有更多知識和經驗的人可以幫助指導我朝著正確的方向前進。我的網絡在 y 值預測上表現良好。但似乎 x 值向原點左移太遠了。似乎如果我將數據向右移動，預測實際上會非常好。下面是預測圖的圖片：我很確定錯誤來自我制作 X_test 值數組時。以下是將以下所有代碼組織成部分：數據集：我的火車數據是特斯拉從 2014 年到 2018 年的 4 年收盤價。我要預測的數據是 2019 年的收盤價。# get 2014-2018 data to train our modelstart = datetime.datetime(2014,1,1)end = datetime.datetime(2018,12,30)df = web.DataReader("TSLA", 'yahoo', start, end) # get 2019 data to test our model on start = datetime.datetime(2019,1,1)end = datetime.date.today()test_df = web.DataReader("TSLA", 'yahoo', start, end) # sort by datedf = df.sort_values('Date')test_df = test_df.sort_values('Date')# fix the date df.reset_index(inplace=True)df.set_index("Date", inplace=True)test_df.reset_index(inplace=True)test_df.set_index("Date", inplace=True)df.tail() High Low Open Close Volume Date 2014-01-02 152.479996 146.550003 149.800003 150.100006 6188400 2014-01-03 152.190002 148.600006 150.000000 149.559998 4695000 2014-01-06 150.399994 145.240005 150.000000 147.000000 5361100 2014-01-07 150.399994 145.250000 147.619995 149.360001 5034100 2014-01-08 153.699997 148.759995 148.850006 151.279999 6163200 ... ... ... ... ... ... 2018-12-24 314.500000 295.200012 313.500000 295.390015 5559900 2018-12-26 326.970001 294.089996 300.000000 326.089996 8163100 2018-12-27 322.170013 301.500000 319.839996 316.130005 8575100 2018-12-28 336.239990 318.410004 323.100006 333.869995 9939000 2018-12-31 339.209991 325.260010 337.790009 332.799988 6302300創建火車數據：# create train set of adj close prices data:train_data = df.loc[:,'Adj Close'].as_matrix()print(train_data.shape) # 1258 for 循環前 train_data 的 len 為 1258，for 循環后為 1222。
1 回答
交互式愛情
TA貢獻1712條經驗 獲得超3個贊
解決方案：
如果有人從谷歌找到這個我想通了。我將訓練和測試數據創建代碼更改為：
'''Function to create a dataset to feed into an LSTM'''
def create_dataset(dataset, look_back):
dataX, dataY = [], []
for i in range(len(dataset)-look_back):
a = dataset[i:(i + look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
# Create the data to train our model on:
time_steps = 36
X_train, y_train = create_dataset(train_data, time_steps)
# reshape it [samples, time steps, features]
X_train = np.reshape(X_train, (X_train.shape[0], 36, 1))
print(X_train.shape) # 1222, 36, 1
測試數據：
# Get the stock prices for 2019 to have our model make the predictions
test_data = test_df['Adj Close'].values
test_data = test_data.reshape(-1,1)
test_data = scaler.transform(test_data)
# Create the data to test our model on:
time_steps = 36
X_test, y_test = create_dataset(test_data, time_steps)
# store the original vals for plotting the predictions
y_test = y_test.reshape(-1,1)
org_y = scaler.inverse_transform(y_test)
# reshape it [samples, time steps, features]
X_test = np.reshape(X_test, (X_test.shape[0], 36, 1))
# Predict the prices with the model
predicted_y = model.predict(X_test)
predicted_y = scaler.inverse_transform(predicted_y)
新預測：
我將其更改為繪制存儲的原始 y val org_y，然后繪制我們預測的 y val
plt.plot(org_y, color = 'red', label = 'Real Tesla Stock Price')
plt.plot(predicted_y, color = 'blue', label = 'Predicted Tesla Stock Price')
plt.title('Tesla Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Tesla Stock Price')
plt.legend()
plt.show()
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