seasonal-patterns

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Original

English

🇨🇳

Translation

Chinese

Seasonal Pattern Detection

季节性规律检测

Overview

概述

Identify cyclical trends in your revenue and expenses over a 12-month or multi-year window. Reveals which months are historically strong or weak, enabling better cash management, staffing, and marketing timing.

识别12个月或多年周期内收入和支出的周期性趋势。揭示历史上表现强劲或疲软的月份，助力更好的现金流管理、人员配置和营销时机规划。

Wilson Tools Used

使用的Wilson工具

```
spending_summary
```
— pull monthly income and expense totals across 12+ months to build a seasonal profile

```
spending_summary
```
— 提取12个月以上的月度收支总额，构建季节性特征档案

Workflow

工作流程

Ask for the analysis window (minimum 12 months, 24+ months preferred for statistical confidence).
Use
```
spending_summary
```
for each month in the window to get monthly revenue and expense totals.
Calculate the monthly index: Month Index = Month Revenue / Average Monthly Revenue * 100.
- Index > 100 = above-average month
- Index < 100 = below-average month
Identify peaks and troughs.
Generate the seasonal profile:

SEASONAL PATTERN ANALYSIS — [Period]
══════════════════════════════════════════════════════════
Month    Avg Revenue   Index   Avg Expenses   Trend
──────────────────────────────────────────────────────────
Jan        $8,200        82      $6,100       Slow start
Feb        $7,800        78      $5,900       ▼ Trough
Mar        $9,500        95      $6,500       Recovering
Apr       $11,200       112      $7,200       ▲ Above avg
May       $12,500       125      $7,800       ▲ Strong
Jun       $13,000       130      $8,200       ▲▲ Peak
Jul       $11,800       118      $7,500       ▲ Above avg
Aug       $10,200       102      $7,000       Average
Sep       $10,500       105      $7,200       Average
Oct       $11,000       110      $7,500       ▲ Above avg
Nov        $9,500        95      $8,500       Expense spike
Dec        $6,800        68      $9,000       ▼▼ Low + high cost
──────────────────────────────────────────────────────────
Annual   $122,000       100     $88,400
Peak Month: June (130)    Trough Month: December (68)
Seasonal Range: 62 points
══════════════════════════════════════════════════════════

Calculate the seasonal range (peak index - trough index). Over 50 points indicates significant seasonality.
Provide actionable recommendations:
- Build cash reserves during peak months for trough months
- Time major expenses (equipment, marketing campaigns) for peak-revenue months
- Consider seasonal pricing or promotions during slow months

询问分析周期（最少12个月，推荐24个月以上以获得统计可信度）。
对周期内的每个月使用
```
spending_summary
```
获取月度收入和支出总额。
计算月度指数：月度指数 = 月度收入 / 平均月度收入 * 100。
- 指数>100 = 表现优于平均水平的月份
- 指数<100 = 表现低于平均水平的月份
识别峰值和谷值。
生成季节性特征档案：

SEASONAL PATTERN ANALYSIS — [Period]
══════════════════════════════════════════════════════════
Month    Avg Revenue   Index   Avg Expenses   Trend
──────────────────────────────────────────────────────────
Jan        $8,200        82      $6,100       Slow start
Feb        $7,800        78      $5,900       ▼ Trough
Mar        $9,500        95      $6,500       Recovering
Apr       $11,200       112      $7,200       ▲ Above avg
May       $12,500       125      $7,800       ▲ Strong
Jun       $13,000       130      $8,200       ▲▲ Peak
Jul       $11,800       118      $7,500       ▲ Above avg
Aug       $10,200       102      $7,000       Average
Sep       $10,500       105      $7,200       Average
Oct       $11,000       110      $7,500       ▲ Above avg
Nov        $9,500        95      $8,500       Expense spike
Dec        $6,800        68      $9,000       ▼▼ Low + high cost
──────────────────────────────────────────────────────────
Annual   $122,000       100     $88,400
Peak Month: June (130)    Trough Month: December (68)
Seasonal Range: 62 points
══════════════════════════════════════════════════════════

计算季节性范围（峰值指数 - 谷值指数）。超过50点表示存在显著季节性。
提供可执行建议：
- 在峰值月份建立现金储备，以备谷值月份使用
- 将重大支出（设备、营销活动）安排在收入峰值月份
- 考虑在淡季推出季节性定价或促销活动

Without Wilson

未使用Wilson的操作流程

Export 12-24 months of transactions from your bank as CSV.

In a spreadsheet, add a

Month

column:

=TEXT(Date,"YYYY-MM")

and a

MonthNum

column:

=MONTH(Date)

Create a pivot table: Rows = MonthNum, Values = Average of Income, Average of Expenses (use separate columns for multi-year averaging).
Overall Average:
```
=AVERAGE(AllMonthlyRevenues)
```
.
Seasonal Index per month:
```
=MonthAvgRevenue/OverallAvg*100
```
.
In Google Sheets, create a line chart of monthly revenue with a trendline to visualize the pattern.
For statistical detection, use Excel's
```
=FORECAST.ETS.SEASONALITY(values, timeline)
```
to auto-detect the seasonal period length.
Google Sheets alternative: chart the data and add a polynomial trendline (order 4-6) to see the seasonal curve.

从银行导出12-24个月的交易记录为CSV格式。

在电子表格中添加

Month

列：

=TEXT(Date,"YYYY-MM")

和

MonthNum

列：

=MONTH(Date)

。

创建数据透视表：行 = MonthNum，值 = 收入平均值、支出平均值（多年平均需使用单独列）。
总体平均值：
```
=AVERAGE(AllMonthlyRevenues)
```
。
月度季节性指数：
```
=MonthAvgRevenue/OverallAvg*100
```
。
在Google Sheets中，创建月度收入折线图并添加趋势线以可视化规律。
如需统计检测，使用Excel的
```
=FORECAST.ETS.SEASONALITY(values, timeline)
```
自动检测季节性周期长度。
Google Sheets替代方案：将数据制成图表并添加多项式趋势线（4-6阶）以查看季节性曲线。

Important Notes

重要说明

You need at least 12 months of data for meaningful seasonal analysis. With only one year, you cannot distinguish seasonal patterns from one-time events. Two or more years dramatically improves confidence.
Not every business is seasonal. If your seasonal range is under 20 points, your revenue is relatively stable month-to-month.
Expense seasonality matters too. Insurance renewals, annual software subscriptions, and quarterly tax payments create predictable expense spikes.
Overlay seasonal patterns onto your cash flow forecast for more accurate projections.

进行有意义的季节性分析至少需要12个月的数据。仅一年的数据无法区分季节性规律和一次性事件。两年或以上的数据能大幅提升分析可信度。
并非所有企业都存在季节性。如果季节性范围低于20点，说明你的月度收入相对稳定。
支出季节性也很重要。保险续保、年度软件订阅和季度税款支付会产生可预测的支出峰值。
将季节性规律叠加到现金流预测中，以获得更准确的预测结果。",