pycon tw 2024 tx sales trend

tx_sales_chart.py

import matplotlib.pyplot as plt
from statsmodels.tsa.holtwinters import ExponentialSmoothing
import numpy as np

# 1. 數據準備
# 2024年銷售數據（從第8天開始）
sales_data_2024 = [34, 66, 68, 70, 73, 75, 79, 83, 88, 92, 94, 98, 102, 109, 112, 113, 117, 118, 120, 121, 122, 123, 128, 136, 140]
sales_data_2024.extend([142, 146, 149, 154, 155, 161, 164, 167, 171, 175, 180, 183, 187, 202, 209, 214, 219, 221, 221]) # Aug 8 added

# 2023年銷售數據
sales_data_2023 = [3, 8, 8, 11, 12, 14, 14, 14, 22, 25, 27, 35, 36, 36, 36, 38, 40, 42, 43, 43, 44, 44, 46, 46, 49, 49, 49, 49, 51, 56, 61, 61, 62, 64, 65, 65, 67, 70, 74, 75, 75, 75, 79, 83, 86, 90, 92, 94, 96, 106, 121, 122, 125, 131, 135, 137, 151, 177, 207, 210, 225, 233, 235, 237, 248, 253, 257, 257, 262, 264, 327, 329, 330, 347, 351, 354, 365, 367, 367, 370, 372, 372, 372, 375, 391, 405, 405, 406, 406, 406, 407, 416, 419, 422, 426, 427, 428, 432, 440, 443, 446, 453, 458, 463, 464, 470, 479, 487, 491, 498, 502, 504, 505, 515, 521, 525, 547]

# 生成對應的天數
days_2024 = list(range(8, len(sales_data_2024) + 8))  # 從第8天開始
days_2023 = list(range(1, len(sales_data_2023) + 1))

# 2. 模型訓練和預測
# 構建指數平滑模型
model = ExponentialSmoothing(sales_data_2024, trend='add', seasonal=None, seasonal_periods=None)
fit = model.fit()

# 預測未來29天的銷售量
forecast_days = list(range(len(sales_data_2024) + 8, len(sales_data_2024) + 37))  # 調整預測天數
sales_forecast_ewm = fit.forecast(len(forecast_days))

# 3. 繪圖
plt.figure(figsize=(12, 6))

# 繪製2024年實際銷售數據
plt.plot(days_2024, sales_data_2024, marker='o', linestyle='-', color='b', label='2024 Actual Sales')

# 繪製2024年預測銷售數據
plt.plot(forecast_days, sales_forecast_ewm, marker='o', linestyle='--', color='r', label='2024 Forecast Sales (EWM)')

# 繪製2023年實際銷售數據
plt.plot(days_2023, sales_data_2023, marker='o', linestyle='-', color='g', label='2023 Actual Sales')

# 標記2024年目標值
target_value = 446
target_day = 80
plt.axvline(x=target_day, color='grey', linestyle='--')
plt.annotate(f'2024 Target: {target_value}', xy=(target_day, target_value),
             xytext=(target_day + 1, target_value + 50),
             arrowprops=dict(facecolor='black', shrink=0.05))

# 設置圖表屬性
plt.xlabel('Day')
plt.ylabel('Sales Quantity')
plt.title('Daily Sales Trend')
plt.grid(True)
plt.xticks(range(1, max(max(forecast_days), len(days_2023)) + 1, 5))
plt.legend()
plt.tight_layout()

# 顯示圖表
plt.show()