Time Series Line Charts
Overview
Line charts display variables over time, showing trends, patterns, and temporal relationships. They're the most common chart type for time series data in MMM.
Purpose: Visualize how variables change over time, identify trends, seasonality, and relationships between time series.
When to Use Line Charts
Best For:
Showing trends over time
Identifying seasonal patterns
Comparing multiple variables temporally
Visualizing time series data
Spotting outliers or anomalies
Examples:
Marketing spend trends over months
KPI performance over time
Comparing TV vs. Digital spend
Viewing adstock effects
Creating a Line Chart
Step 1: Select Variables
Choose 1-8 variables (2-5 recommended)
Can include KPI for comparison
Search to filter if many variables
Step 2: Choose Line Chart Type
Click "Line Chart" button
Icon: 📈 Trending up
Step 3: Configure Options
Dual Y-Axis: Use if variables have different scales
Include KPI: Add target variable to chart
Step 4: Generate
Click "Generate Chart"
Chart appears in right panel
Chart Elements
X-Axis (Time):
Date labels
Monthly intervals by default
Adjusts to data frequency
Y-Axis (Value):
Variable values
Auto-scaled to data range
Left axis for all variables (or first half if dual axis)
Lines:
Each variable is a different colored line
2-pixel width
Markers at data points
Legend:
Shows variable names and colors
Click to hide/show series
Bottom or side position
Dual Y-Axis
When to Use:
Variables with very different scales
Example: Spend ($1000s) vs. CTR (0-5%)
Prevents smaller values from being flattened
How It Works:
First half of selected variables → Left Y-axis
Second half → Right Y-axis (opposite side)
Each axis scales independently
Example:
Reading Line Charts
Trends:
Upward slope = increasing
Downward slope = decreasing
Flat = stable
Seasonality:
Regular up-and-down patterns
Annual, quarterly, or monthly cycles
Peaks at same times each year
Correlations:
Lines moving together = positive correlation
Lines moving opposite = negative correlation
No pattern = little correlation
Outliers:
Sharp spikes or dips
Points far from trend
May indicate data issues or special events
Common Patterns
Growing Trend:
Consistent upward movement
Marketing increasing over time
Business growth
Declining Trend:
Consistent downward movement
Channel being phased out
Market contraction
Seasonal Pattern:
Regular cyclical movements
Holiday peaks
Summer dips
Volatile:
Erratic up and down
Campaign-driven spend
Promotional effects
Stable:
Relatively flat line
Consistent always-on spend
Baseline performance
Interactive Features
Zoom:
Mouse wheel to zoom in/out
Selection box (click and drag)
Focus on specific time periods
Pan:
Drag left/right when zoomed
Navigate across timeline
Tooltip:
Hover over line for exact values
Shows date and value
Multiple series shown
Legend Click:
Hide/show individual lines
Isolate specific variables
Compare subsets
Reset:
Toolbar button
Return to full view
Clear all zooms
Use Cases
Trend Analysis
Identify direction:
Seasonal Detection
Find patterns:
Variable Comparison
Compare channels:
Correlation Check
Validate relationships:
Best Practices
Variable Count:
2-3 variables: Easy to read
4-5 variables: Still manageable
6-8 variables: Gets cluttered
8 variables: Use multiple charts
Scale Considerations:
Similar scales: Single Y-axis
Different scales: Dual Y-axis
Mix types: Consider separate charts
Time Range:
Full history for trends
Zoom for detailed periods
Compare same periods year-over-year
Color Choice:
Automatic colors usually fine
Distinct colors help differentiation
Consider colorblind-friendly palettes
Troubleshooting
Lines are flat/compressed: → Use dual Y-axis if scales very different
Too cluttered: → Reduce number of variables → Use legend to hide some series → Create multiple charts
Missing data points: → Check data upload → Verify variable exists in all periods → Review data quality
Can't see pattern: → Zoom into specific period → Check if seasonal (need full year) → Try different variables
Examples
Example 1: Marketing Spend Trends
Variables: TV_Spend, Digital_Spend, Radio_Spend Observation: Digital increasing, TV stable, Radio declining Insight: Budget shifting to digital
Example 2: KPI with Seasonality
Variables: Sales_KPI Observation: Regular Q4 peaks, Q1 dips Insight: Strong holiday seasonality, plan budgets accordingly
Example 3: Spend vs. Performance
Variables: Total_Spend, KPI Configuration: Dual Y-axis Observation: Both trending up, KPI faster than spend Insight: Improving efficiency over time
Next Steps
After creating line charts:
Use Scatter Plots to explore specific relationships
Check Correlation Matrix for all variable pairs
Apply insights in Model Building
Compare with Decomposition results after modeling
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