LSTM时间序列预测结果绘图异常，寻求技术解决方案

我绘制时间序列数据点时遇到问题，图表显示异常，缺乏相关经验无法解决。附上当前绘图效果截图、绘图代码及完整预测实现代码，期望得到正常的时间序列图。感谢帮助。

绘图代码 #

import matplotlib.pyplot as plt

# Plot actual and predicted values
plt.figure(figsize=(12, 6))
plt.plot(results_df.index, results_df['Actual'], label='Actual', color='blue')
plt.plot(results_df.index, results_df['Predicted'], label='Predicted', color='red')
plt.xlabel('Date')
plt.ylabel('Price (Scaled)')
plt.title('Actual vs. Predicted Prices')
plt.legend()
plt.grid(True)
plt.show()

完整预测代码 #

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from math import sqrt

# Function to convert series to supervised learning
def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
    n_vars = 1 if type(data) is list else data.shape[1]
    df = pd.DataFrame(data)
    cols, names = list(), list()
    # input sequence (t-n, ... t-1)
    for i in range(n_in, 0, -1):
        cols.append(df.shift(i))
        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]
    # forecast sequence (t, t+1, ... t+n)
    for i in range(0, n_out):
        cols.append(df.shift(-i))
        if i == 0:
            names += [('var%d(t)' % (j+1)) for j in range(n_vars)]
        else:
            names += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]
    # put it all together
    agg = pd.concat(cols, axis=1)
    agg.columns = names
    # drop rows with NaN values
    if dropnan:
        agg.dropna(inplace=True)
    return agg

# Load dataset
dataset = pd.read_csv('cleaned_merged_dataset.csv', header=0, index_col=0, parse_dates=True)

# Separate the features for scaling
price = dataset.values[:, :1]
wind_gen = dataset.values[:, 1:]

# Create separate scalers for price and wind generation
price_scaler = MinMaxScaler(feature_range=(0, 1))
wind_gen_scaler = MinMaxScaler(feature_range=(0, 1))

# Fit and transform the features
scaled_price = price_scaler.fit_transform(price)
scaled_wind_gen = wind_gen_scaler.fit_transform(wind_gen)

# Combine the scaled features
scaled_values = np.concatenate((scaled_price, scaled_wind_gen), axis=1)

# Frame as supervised learning
reframed = series_to_supervised(scaled_values, 1, 1)

# Split into train and test sets
values = reframed.values
n_train_days = 365  # Use first year for training
train = values[:n_train_days, :]
test = values[n_train_days:, :]

# Split into input and outputs
train_X, train_y = train[:, :-1], train[:, -1]
test_X, test_y = test[:, :-1], test[:, -1]

# Reshape input to be 3D [samples, timesteps, features]
train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))
test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1]))

# Define LSTM model
model = Sequential()
model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dense(1))
model.compile(loss='mae', optimizer='adam')

# Fit the model
model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False)

# Make a prediction
yhat = model.predict(test_X)

# Invert scaling for forecast
yhat_reshaped = yhat.reshape(-1, 1)
inv_yhat = price_scaler.inverse_transform(yhat_reshaped)
inv_yhat = inv_yhat[:, 0]

# Invert scaling for actual
test_y_reshaped = test_y.reshape(-1, 1)
inv_y = price_scaler.inverse_transform(test_y_reshaped)
inv_y = inv_y[:, 0]

# Calculate RMSE
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
print('Test RMSE: %.3f' % rmse)

# Get the date index for the test set from the original merged dataset
test_dates = dataset.index[n_train_days+1:]

# Create the results DataFrame
results_df = pd.DataFrame(data={"Date": test_dates, "Actual": inv_y, "Predicted": inv_yhat})

# Set 'Date' as the index
results_df.set_index('Date', inplace=True)

我此前尝试过多种方法但未能解决该问题，仅期望得到正常的时间序列图。

内容的提问来源于stack exchange，提问作者Civan Ridvan Muhsinoglu