import pandas as pd from pyecharts import options as opts from pyecharts.charts import Line # 读取Excel文件 data = pd.read_excel('6004020918.xlsx') # 提取数据 week = data['week'] need = data['need'] # 定义三步指数平滑函数 def triple_exponential_smoothing(series, alpha, beta, gamma, n_preds): result = [series[0]] season_length = len(series) // n_preds # 初始化水平、趋势和季节性指数 level, trend, season = series[0], series[1] - series[0], sum(series[:season_length]) / season_length for i in range(1, len(series) + n_preds): if i >= len(series): # 预测新值 m = i - len(series) + 1 result.append(level + m * trend + season) else: # 更新水平、趋势和季节性指数 value = series[i] last_level, level = level, alpha * (value - season) + (1 - alpha) * (level + trend) trend = beta * (level - last_level) + (1 - beta) * trend season = gamma * (value - level) + (1 - gamma) * season result.append(level + trend + season) return result # 设置三步指数平滑法参数 alpha = 0.2 beta = 0.3 gamma = 0.4 n_preds = 77 # 预测的值数量 # 进行三步指数平滑预测 predictions = triple_exponential_smoothing(need[:100], alpha, beta, gamma, n_preds) # 创建折线图对象 line = Line() line.set_global_opts(title_opts=opts.TitleOpts(title='时间序列预测分析'), legend_opts=opts.LegendOpts(data=['预测值', '实际值'])) # 添加预测值数据 line.add_xaxis(week[100:177]) line.add_yaxis('预测值', predictions, is_smooth=True) # 添加实际值数据 line.add_yaxis('实际值', need[100:177], is_smooth=True) # 生成HTML文件 line.render('time_series_forecast.html')这个代码出现了TypeError: init() got an unexpected keyword argument 'data'这个问题，把修改好的代码给我

非常抱歉给您带来了困扰。根据您提供的信息，我发现问题出在了 `line.set_global_opts()` 方法中，其中的 `legend_opts` 参数有误。

以下是修改后的代码：

import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Line

# 读取Excel文件
data = pd.read_excel('6004020918.xlsx')

# 提取数据
week = data['week']
need = data['need']

# 定义三步指数平滑函数
def triple_exponential_smoothing(series, alpha, beta, gamma, n_preds):
    result = [series[0]]
    season_length = len(series) // n_preds
    
    # 初始化水平、趋势和季节性指数
    level, trend, season = series[0], series[1] - series[0], sum(series[:season_length]) / season_length
    
    for i in range(1, len(series) + n_preds):
        if i >= len(series):
            # 预测新值
            m = i - len(series) + 1
            result.append(level + m * trend + season)
        else:
            # 更新水平、趋势和季节性指数
            value = series[i]
            last_level, level = level, alpha * (value - season) + (1 - alpha) * (level + trend)
            trend = beta * (level - last_level) + (1 - beta) * trend
            season = gamma * (value - level) + (1 - gamma) * season
            result.append(level + trend + season)
    
    return result

# 设置三步指数平滑法参数
alpha = 0.2
beta = 0.3
gamma = 0.4
n_preds = 77  # 预测的值数量

# 进行三步指数平滑预测
predictions = triple_exponential_smoothing(need[:100], alpha, beta, gamma, n_preds)

# 创建折线图对象
line = Line()
line.set_global_opts(
    title_opts=opts.TitleOpts(title='时间序列预测分析'),
    legend_opts=opts.LegendOpts(data=[opts.LegendOpts.NameOpts(name='预测值'), opts.LegendOpts.NameOpts(name='实际值')])
)

# 添加预测值数据
line.add_xaxis(week[100:177])
line.add_yaxis('预测值', predictions, is_smooth=True)

# 添加实际值数据
line.add_yaxis('实际值', need[100:177], is_smooth=True)

# 生成HTML文件
line.render('time_series_forecast.html')

修正后的代码中，我将 `legend_opts` 参数修改为了一个包含两个 `NameOpts` 对象的列表，用于设置图例的名称。

请尝试使用这个修正后的代码，并检查是否还有其他可能导致问题的错误。如果问题仍然存在，请提供完整的错误消息和堆栈跟踪，以便更好地帮助您解决问题。