Time Series Analysis Forecasting.

 Key Focus: Time Series Analysis, Forecasting, Seasonality, Outlier Detection, Trend Lines.

Scenario:

1) You need to create a timeseries analysis dashboard for an e-commerce/ retail company to forecast future sales based on historical data. 

2) The dashboard should include monthly, quarterly, and yearly sales trends, seasonal patterns, and predictive forecasts. 

3) Explain how you would handle seasonality, outliers, and trend analysis in Power bi, and how you would implement advanced forecasting techniques to provide accurate predictions.

✅ Dashboard Elements Checklist:

Element	Type	Purpose
Monthly/Quarterly Trend	Line/Area Chart	Observe short and mid-term movement
Year-over-Year Comparison	Column Chart	Detect long-term growth/shrinkage
Seasonality Matrix	Matrix/Heatmap	Identify recurring high/low sales periods
Moving Average	Line Overlay	Smooth volatility
Outlier Detection	Conditional Color	Spot abnormal behavior
Forecast Visual	Line Chart (w/ forecast)	Predict future sales
Slicers	Year/Region/Product	Dynamic filtering

📂 Sample PBIX File Structure (Sections & Pages)

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🧱 1. Data Model

Tables:

• Sales (Fact Table)

  • Date

  • Product ID

  • Customer ID

  • Quantity

  • SalesAmount

• Date (Date Dimension)

  • Date

  • Year, Month, Quarter

  • MonthName, DayOfWeek, WeekNumber

  • YearMonth (concatenated)

• Optional:

  • Products, Categories, Customers, Regions

Relationships:

• Sales[Date] → Date[Date]

• Sales[Product ID] → Products[Product ID]

• etc.

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📊 2. Report Pages

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📈 Page 1: Sales Overview (Time Series)

Visuals:

• Line Chart: Date[Date] vs Sales[SalesAmount] (monthly granularity)

• Slicer: Year / Month

• Card: Total Sales, YoY %, MoM %

• Line Chart: SalesAmount + 3-month Moving Average

• Forecast: Enable forecast in analytics pane

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🧭 Page 2: Seasonality Analysis

Visuals:

• Column Chart: MonthName vs Avg(SalesAmount) across years

• Heatmap or Matrix: Year on rows, MonthName on columns → SUM(SalesAmount)

• Line Chart: WeekNumber vs SalesAmount

• Tooltip: Custom tooltip showing % change YoY for each month

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🚨 Page 3: Outlier Detection

Visuals:

• Line Chart: Daily/weekly sales with color-coded outliers

• Table: Date | Sales | Z-Score | Outlier Flag

• KPI: Total # of outliers

In Power Query:

• Add Z-score column:

  M
  
  ZScore = (SalesAmount - Avg) / StdDev
  

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🔮 Page 4: Forecasting (Advanced)

Options:

• Option A: Use built-in Forecast (Analytics pane in line chart)

• Option B: Use Python/R Visual:

  • Insert a script using Prophet/ARIMA

  • Output: Date vs Forecasted Sales

• Option C: Table of predicted sales for next 3/6/12 months

Slicers: Product Category, Region (to forecast per segment)

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⚙️ Page 5: What-If Scenarios

Elements:

• What-If parameter: % increase in traffic or marketing budget

• DAX measure:

  Adjusted Sales = [SalesAmount] * (1 + 'Scenario'[Increase])

• Line chart comparing Actual vs Adjusted Sales

• Forecast line with scenario applied

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🔧 Measures (DAX)

• Total Sales = SUM(Sales[SalesAmount])

• YoY %:

  YoY Sales % = 
  DIVIDE(
      [Total Sales] - CALCULATE([Total Sales], SAMEPERIODLASTYEAR(Date[Date])),
      CALCULATE([Total Sales], SAMEPERIODLASTYEAR(Date[Date]))
  )
  

• 3M Moving Avg (as above)

• Z-Score, Outlier Flag

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📦 Export & Sharing Options

• Enable Row-level Security (RLS) if needed (e.g., sales region managers).

• Publish to Power BI Service.

• Enable scheduled refresh.

• Share dashboard with stakeholders or embed in web/app.