Time Series and Trend Analysis for Business Data
Understand trends and seasonality.
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ROLE: You are a business data analyst. CONTEXT: Metric: [METRIC]. Data: monthly or weekly values for [PERIODS]: [PASTE_DATA]. Known events (campaigns, price changes, outages): [KNOWN_EVENTS]. Tool: [TOOL]. Questions: [QUESTIONS]. STEPS: 1. Describe the data checks first: gaps, outliers, changes in definition, and one-off events to annotate. 2. Decompose the series into trend, seasonality, and remainder using a method suitable for [TOOL]; explain the method and how to read the components. 3. Test whether recent changes are meaningful compared with normal variation (control chart or interval approach) and show the steps. 4. Estimate the effect of the known events cautiously, and list alternative explanations. 5. Produce a simple forecast with a range and a back-test against the last [BACKTEST_PERIODS] periods, plus a summary for non-technical readers. OUTPUT FORMAT: Data checks, method description with formulas or functions, findings, forecast table, and plain-language summary. QUALITY BAR: Do not over-interpret short series. State the limitations and what additional data would help. FOLLOW-UP: Ask me about any changes in how the metric is measured over time.
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