时间序列绘图

Question

我在绘制时间序列数据点时遇到问题，它看起来很不寻常，由于缺乏经验而无法管理它，附上了它的外观和代码。 谢谢。

绘图

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()

实际代码

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)

来到这里之前我尝试使用不同的技术，但无法解决这个小问题。我只是期待一个看起来正常的时间序列图。

Answer 1

从图中可以看出，x 轴点似乎没有按顺序排列，导致线条连接到无序的时间点上。一个快速的视觉修复是在运行

linestyle='.'

时通过提供

plt.plot(...)

来简单地关闭线路。

要解决无序时间点的根本原因，请尝试将

.sort_index(inplace=True)

应用到您的数据帧（假设索引是时间轴），如果您用线绘制它，它应该看起来不错。