Time series description is the NLP task of generating natural language descriptions of temporal data patterns — automatically converting time-ordered numerical data (trends, seasonalities, anomalies, changepoints) into readable text that explains what happened, when, and why it matters, enabling automated reporting and data narration for temporal datasets.

What Is Time Series Description?

• Definition: Generating text that describes patterns in time series data.
• Input: Time-ordered numerical data (metrics, KPIs, sensor readings).
• Output: Natural language description of trends, patterns, and events.
• Goal: Make temporal data patterns accessible through text.

Why Time Series Description?

• Automation: Generate commentary for dashboards and reports automatically.
• Accessibility: Not everyone can read line charts — text is universal.
• Attention: Highlight important changes that might be missed in charts.
• Context: Explain what patterns mean for the business or domain.
• Scale: Describe thousands of time series simultaneously.
• Alerts: Narrative explanations of triggered anomalies.

Time Series Patterns to Describe

Trends:

• Upward Trend: "Revenue grew steadily from $1M to $1.5M over Q1-Q3."
• Downward Trend: "Daily active users declined 12% over the past month."
• Flat/Stable: "Manufacturing yield remained stable at 95.2% ± 0.3%."
• Acceleration/Deceleration: "Growth rate accelerated from 3% to 7% monthly."

Seasonality:

• Weekly: "Traffic peaks on Tuesdays and drops on weekends."
• Monthly: "Sales consistently spike in the last week of each month."
• Annual: "Q4 accounts for 40% of annual revenue due to holiday demand."

Anomalies:

• Spikes: "Server latency spiked to 500ms at 2:30 PM (normal: 50ms)."
• Drops: "Conversion rate unexpectedly dropped 40% on March 15."
• Outliers: "Three data points significantly exceeded the 99th percentile."

Changepoints:

• Level Shift: "Average order value increased permanently from $45 to $62 after the pricing change."
• Trend Change: "Growth shifted from 5% to 12% monthly following the product launch."

Comparisons:

• Period-over-Period: "Revenue is up 15% vs. same period last year."
• Target vs. Actual: "Quality metrics are 3% below the quarterly target."
• Benchmark: "Our NPS of 72 is 15 points above industry average."

Description Generation Pipeline

1. Pattern Detection:

• Trend analysis (linear regression, moving averages).
• Seasonality decomposition (STL, Fourier).
• Anomaly detection (Z-score, isolation forest).
• Changepoint detection (PELT, Bayesian).

2. Significance Assessment:

• Statistical significance of trends and changes.
• Business significance (materiality thresholds).
• Rank patterns by importance for reporting.

3. Content Selection:

• Choose most important patterns to describe.
• Consider audience (executive summary vs. detailed analysis).
• Prioritize actionable insights over routine observations.

4. Narrative Generation:

• Generate natural language for each selected pattern.
• Add context (comparisons, targets, historical norms).
• Structure into coherent narrative (most important first).

5. Contextualization:

• Link patterns to known events or causes.
• Provide domain-specific interpretation.
• Suggest implications and recommended actions.

AI Approaches

Rule-Based NLG:

• Pattern → template mapping.
• Example: IF trend > 10% THEN "significant increase."
• Benefit: Precise, predictable output.
• Limitation: Limited vocabulary and variation.

Neural NLG:

• Train models on (time series, description) pairs.
• End-to-end pattern detection and verbalization.
• Benefit: More natural, varied language.
• Challenge: Training data scarcity.

LLM-Based:

• Provide time series statistics in prompt.
• LLM generates natural language description.
• Benefit: Excellent language quality, easy to implement.
• Challenge: Must pre-compute statistics (LLMs can't process raw series well).

Numerical Precision

• Rounding: Appropriate precision for audience (executives: round numbers; analysts: exact).
• Units: Consistent unit usage with conversions where needed.
• Percentages: Clear base and direction ("up 15% from Q1" vs "15% of total").
• Comparisons: Fair comparisons (same time period, same scope).

Applications

• Business Dashboards: Auto-generated narrative beneath charts.
• Financial Reports: Describe stock performance, revenue trends.
• Healthcare: Patient vital signs trending, lab result changes.
• IoT/Manufacturing: Sensor reading summaries, process monitoring.
• Weather: Historical weather pattern descriptions.
• Sports: Performance statistics narration.

Tools & Platforms

• NLG Platforms: Arria, Automated Insights (Wordsmith), Narrative Science (Quill).
• BI Integration: Power BI Smart Narratives, Tableau Explain Data.
• Custom: LLM APIs with time series preprocessing.
• Libraries: Prophet (forecasting), stumpy (matrix profiles), tsfel (features).

Time series description is essential for data-driven storytelling — it transforms the patterns hidden in temporal data into clear, actionable narratives that enable faster understanding and decision-making, ensuring important trends and anomalies don't go unnoticed in seas of numbers and charts.