绘制图形失败和精度

我是机器学习的新手，我正在尝试通过keras预测里拉汇率。我认为这些值是正确的，但是我无法正确绘制这些值。看起来像这样：图片

这是我的代码（csv文件是德语的，因此是这样的翻译：Datum-> Date，Erster-> Open，Hoch-> High，Tief-> Low，Schlusskurs-> Close）：

问题如下：

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM

X_train = []
y_train = []

csv_file = "wkn_A0C32V_historic.csv" #csv file (path)
data = pd.read_csv(csv_file, sep=";") #reading the csv file

data["Erster vorher"] = data["Erster"].shift(-1) #moving the data in Erster(Open) one step backwards
data["Erster"] = data["Erster"].str.replace(",", ".") #replacing all commas with dots in order to calculate with float numbers
data["Erster vorher"] = data["Erster vorher"].str.replace(",", ".") #same here
data["Changes"] = (data["Erster"].astype(float) / data["Erster vorher"].astype(float)) - 1 #calculating the changes

data = data.dropna() #dropping the NaNs
changes = data["Changes"]


#X_train = (number_of_examples, sequence_length, input_dimension)
for i in range(len(changes) - 20):
    X_train.append(np.array(changes[i+1:i+21][::-1]))
    y_train.append(changes[i])
X_train = np.array(X_train).reshape(-1, 20, 1)
y_train = np.array(y_train)



print("X_train shape: " + str(X_train.shape))
print("y_train shape: " + str(y_train.shape))



#Training the data
model = Sequential()
model.add(LSTM(1, input_shape=(20, 1)))
model.compile(optimizer="rmsprop", loss="mse", metrics=["accuracy"])
model.fit(X_train, y_train, batch_size=32, epochs=10)
preds = model.predict(X_train)



preds = preds.reshape(-1)
print("Shape of predictions: " + str(preds.shape))
preds = np.append(preds, np.zeros(20))
data["predictions"] = preds
data["Open_predicted"] = data["Erster vorher"].astype(float) * (1 + data["predictions"].astype(float)) #calculating the new Open with the predicted numbers
print(data)


import matplotlib.pyplot as plt
dates = np.array(data["Datum"]).astype(np.datetime64)
#HERE BEGINS THE PROBLEM...
plt.plot(dates, data["Erster"], label="Erster")
plt.plot(dates, data["Open_predicted"], label="Erster (predicted)")
plt.legend()
plt.show()

输出：

Epoch 9/10

  32/3444 [..............................] - ETA: 0s - loss: 9.5072e-05 - accuracy: 0.1250
 448/3444 [==>...........................] - ETA: 0s - loss: 1.8344e-04 - accuracy: 0.0513
 960/3444 [=======>......................] - ETA: 0s - loss: 1.2734e-04 - accuracy: 0.0583
1472/3444 [===========>..................] - ETA: 0s - loss: 1.0480e-04 - accuracy: 0.0577
1984/3444 [================>.............] - ETA: 0s - loss: 9.7956e-05 - accuracy: 0.0600
2464/3444 [====================>.........] - ETA: 0s - loss: 9.0399e-05 - accuracy: 0.0621
2976/3444 [========================>.....] - ETA: 0s - loss: 8.5287e-05 - accuracy: 0.0649
3444/3444 [==============================] - 0s 122us/step - loss: 8.1555e-05 - accuracy: 0.0633
Epoch 10/10

  32/3444 [..............................] - ETA: 0s - loss: 5.5561e-05 - accuracy: 0.0312
 544/3444 [===>..........................] - ETA: 0s - loss: 6.1705e-05 - accuracy: 0.0662
1056/3444 [========>.....................] - ETA: 0s - loss: 1.2215e-04 - accuracy: 0.0644
1536/3444 [============>.................] - ETA: 0s - loss: 9.9676e-05 - accuracy: 0.0651
2048/3444 [================>.............] - ETA: 0s - loss: 9.2219e-05 - accuracy: 0.0625
2592/3444 [=====================>........] - ETA: 0s - loss: 8.8050e-05 - accuracy: 0.0625
3104/3444 [==========================>...] - ETA: 0s - loss: 8.1685e-05 - accuracy: 0.0651
3444/3444 [==============================] - 0s 118us/step - loss: 8.1349e-05 - accuracy: 0.0633
Shape of predictions: (3444,)
           Datum  Erster    Hoch  ...   Changes predictions Open_predicted
0     2020-09-04  8.8116  8,8226  ...  0.011816    0.000549       8.713479
1     2020-09-03  8.7087  8,8263  ... -0.006457    0.001141       8.775301
2     2020-09-02  8.7653  8,7751  ... -0.005051    0.001849       8.826093
3     2020-09-01  8.8098  8,8377  ...  0.009465    0.001102       8.736818
4     2020-08-31  8.7272  8,7993  ...  0.000069    0.001149       8.736630
...          ...     ...     ...  ...       ...         ...            ...
3459  2009-01-07  2.0449  2,1288  ... -0.021392    0.000000       2.089600
3460  2009-01-06  2.0896  2,0922  ... -0.020622    0.000000       2.133600
3461  2009-01-05  2.1336  2,1477  ...  0.002914    0.000000       2.127400
3462  2009-01-04  2.1274  2,1323  ... -0.005377    0.000000       2.138900
3463  2009-01-02  2.1389  2,1521  ...  0.000000    0.000000       2.138900

[3464 rows x 9 columns]