我使用ARIMA来预测数据集未来5天的金额（Amt）。条形图需要显示每个时间值的数量的串联，然后是未来的预测。
一个dataframe的例子，它比这个大得多：

CCY Pair        Time   Amt
0   GBPUSD  13/05/2023  1000
1   EURUSD  13/05/2023  2000
2   EURUSD  14/05/2023  3000
3   EURUSD  14/05/2023  5000
4   GBPEUR  15/05/2023  4000

字符串
当我尝试绘制模型时，下面的代码给出了以下错误：

Traceback (most recent call last):
  File "Graphs.py", line 46, in <module>
    plt.bar(combined_data.index, combined_data['Amt'])
  File "/opt/homebrew/lib/python3.11/site-packages/matplotlib/pyplot.py", line 2439, in bar
    return gca().bar(
           ^^^^^^^^^^
  File "/opt/homebrew/lib/python3.11/site-packages/matplotlib/__init__.py", line 1442, in inner
    return func(ax, *map(sanitize_sequence, args), **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/lib/python3.11/site-packages/matplotlib/axes/_axes.py", line 2460, in bar
    raise TypeError(f'the dtypes of parameters x ({x.dtype}) '
TypeError: the dtypes of parameters x (object) and width (float64) are incompatible

型
代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.arima.model import ARIMA

# The "Time" column contains the time periods, and "Amt" contains the values
df = pd.read_csv("Data.csv")

# Convert the "Time" column to a datetime type
df['Time'] = pd.to_datetime(df['Time'], dayfirst=True)

# Set the "Time" column as the index
df.set_index('Time', inplace=True)

# Sort the DataFrame by the index
df.sort_index(inplace=True)

df.index = pd.to_datetime(df.index).to_period('D')

# Prepare the data for ARIMA modeling
data = df['Amt']

# Fit the ARIMA model
model = ARIMA(data, order=(1,0,0)) 
model_fit = model.fit()

# Predict the next x periods
x = 5  # Number of periods to predict
predictions = model_fit.forecast(steps=x)

# Generate the next x bar charts based on the predictions
next_time_periods = pd.date_range(start=df.index.max().to_timestamp() + pd.DateOffset(days=1), periods=x, freq='D')  # Generate x future time periods
next_bar_charts = pd.DataFrame({'Time': next_time_periods, 'Amt': predictions}, index=next_time_periods)

# Concatenate current and predicted bar chart data
combined_data = pd.concat([df, next_bar_charts])

# Plot the combined bar chart
plt.figure(figsize=(10, 6))
plt.bar(combined_data.index, combined_data['Amt'])
plt.xlabel('Time')
plt.ylabel('Amt')
plt.xticks(rotation=45)  # Rotate x-axis labels for better readability
plt.show()

型