Context and Data Preparation
Analysis Overview and Data Quality
Analysis Overview
Simple Time Series Trend Analysis
Analysis overview and configuration
test_1773162929
Key Insights
Analysis Overview
Purpose
This analysis examines a 5-year daily time series (2013–2017) to identify directional trends and underlying patterns. The objective is to determine whether values are increasing, decreasing, or stable, and to assess what smoothing reveals about the true signal beneath daily volatility.
Key Findings
- Trend Direction: Increasing with a linear slope of 0.004 per day, moving from 13 (Jan 1, 2013) to 23 (Dec 31, 2017)—a 77% percent change over the period
- Trend Strength (R²): 0.105 indicates a weak linear fit; daily values fluctuate considerably around the trend line
- Period Comparison: Second half mean (21.92) exceeds first half mean (18.02) by 22%, confirming upward momentum
- Rolling Average Pattern: The 7-day rolling window smooths noise and reveals the underlying trend more clearly, with trend values ranging from 16.2 to 23.75
Interpretation
The data exhibits a genuine but modest upward trend over five years. While the linear model explains only 10.5% of variance, the period comparison and rolling average both confirm consistent improvement in the second half. Daily observations range widely (4–50), creating noise that masks the signal; the
Key Insights
Analysis Overview
Purpose
This analysis examines a 5-year daily time series (2013–2017) to identify directional trends and underlying patterns. The objective is to determine whether values are increasing, decreasing, or stable, and to assess what smoothing reveals about the true signal beneath daily volatility.
Key Findings
- Trend Direction: Increasing with a linear slope of 0.004 per day, moving from 13 (Jan 1, 2013) to 23 (Dec 31, 2017)—a 77% percent change over the period
- Trend Strength (R²): 0.105 indicates a weak linear fit; daily values fluctuate considerably around the trend line
- Period Comparison: Second half mean (21.92) exceeds first half mean (18.02) by 22%, confirming upward momentum
- Rolling Average Pattern: The 7-day rolling window smooths noise and reveals the underlying trend more clearly, with trend values ranging from 16.2 to 23.75
Interpretation
The data exhibits a genuine but modest upward trend over five years. While the linear model explains only 10.5% of variance, the period comparison and rolling average both confirm consistent improvement in the second half. Daily observations range widely (4–50), creating noise that masks the signal; the
Data Preprocessing
Data Quality & Completeness
Data preprocessing and column mapping
Key Insights
Data Preprocessing
Purpose
This section documents the data preprocessing pipeline for a 5-year daily time series analysis (2013–2017). It shows that no data loss occurred during cleaning, meaning the dataset entered analysis in its original state. This is critical for understanding whether the subsequent trend and distribution findings are based on complete, unfiltered observations.
Key Findings
- Retention Rate: 100% (1,826 rows preserved) – No observations were removed during preprocessing, indicating either pristine data quality or minimal validation criteria applied.
- Rows Removed: 0 – No filtering, deduplication, or outlier removal was performed before trend analysis.
- Train/Test Split: Not documented – The analysis does not explicitly separate data for model validation, limiting ability to assess generalization performance of the linear trend model (R² = 0.105).
Interpretation
The complete retention suggests the raw dataset required no cleaning for missing values, duplicates, or invalid entries. However, this also means no outliers were removed before fitting the linear trend, which may explain the weak R² value (0.105). The lack of a documented train/test split raises concerns about whether the trend slope (0.004) and model fit were validated on held-out data or simply fitted to the entire 1,826-observation window.
Context
The absence of preprocessing transformations (scaling, aggregation beyond
Key Insights
Data Preprocessing
Purpose
This section documents the data preprocessing pipeline for a 5-year daily time series analysis (2013–2017). It shows that no data loss occurred during cleaning, meaning the dataset entered analysis in its original state. This is critical for understanding whether the subsequent trend and distribution findings are based on complete, unfiltered observations.
Key Findings
- Retention Rate: 100% (1,826 rows preserved) – No observations were removed during preprocessing, indicating either pristine data quality or minimal validation criteria applied.
- Rows Removed: 0 – No filtering, deduplication, or outlier removal was performed before trend analysis.
- Train/Test Split: Not documented – The analysis does not explicitly separate data for model validation, limiting ability to assess generalization performance of the linear trend model (R² = 0.105).
Interpretation
The complete retention suggests the raw dataset required no cleaning for missing values, duplicates, or invalid entries. However, this also means no outliers were removed before fitting the linear trend, which may explain the weak R² value (0.105). The lack of a documented train/test split raises concerns about whether the trend slope (0.004) and model fit were validated on held-out data or simply fitted to the entire 1,826-observation window.
Context
The absence of preprocessing transformations (scaling, aggregation beyond
Executive Summary
Key Findings and Recommendations
Executive Summary
Key Findings & Recommendations
Key Performance Indicators
Key Findings
Key findings
| Finding | Detail |
|---|---|
| Trend Direction | Increasing |
| Overall Change | +76.9% (+10.00 total) |
| Trend Strength | Weak (R² < 0.4) |
| Recommendation | Monitor growth rate, identify drivers |
Executive Summary
Bottom Line: Values are trending upward (+76.9% overall) over the period 2013-01-01 to 2017-12-31.
Key Findings:
• The linear trend line has R-squared = 0.105, indicating weak fit
• The value changed from 13.00 to 23.00 (+76.9% total change)
• Rolling average with window = 7 smooths daily noise to reveal underlying pattern
• Period comparison shows second half improved relative to first half
Recommendation: Monitor the growth rate to ensure sustainability. Identify and amplify the drivers of this positive trend.
Key Insights
Executive Summary
Purpose
This executive summary synthesizes five years of daily observations (2013–2017) to assess whether the measured metric achieved meaningful growth. The analysis evaluates trend strength, magnitude of change, and period-over-period performance to inform strategic decision-making about the underlying business driver.
Key Findings
- Trend Direction: Increasing across the full 5-year period, with values rising from 13 to 23
- Percent Change: 76.9% total growth from first to last observation, representing substantial absolute improvement
- Model Fit (R²): 0.105 indicates the linear trend explains only ~10.5% of daily variance, suggesting significant noise and non-linear dynamics
- Period Comparison: Second half mean (21.92) exceeded first half (18.02) by 21.7%, confirming acceleration in later years
- Volatility: Standard deviation of 6.74 and range of 4–50 show considerable daily fluctuation despite upward trajectory
Interpretation
The metric demonstrates genuine upward momentum over the 5-year window, with the second half substantially outperforming the first. However, the weak R² signals that daily values are driven by factors beyond a simple linear progression—likely including seasonal patterns, external shocks, or operational variability. The 77% cumulative gain is meaningful, but
Key Insights
Executive Summary
Purpose
This executive summary synthesizes five years of daily observations (2013–2017) to assess whether the measured metric achieved meaningful growth. The analysis evaluates trend strength, magnitude of change, and period-over-period performance to inform strategic decision-making about the underlying business driver.
Key Findings
- Trend Direction: Increasing across the full 5-year period, with values rising from 13 to 23
- Percent Change: 76.9% total growth from first to last observation, representing substantial absolute improvement
- Model Fit (R²): 0.105 indicates the linear trend explains only ~10.5% of daily variance, suggesting significant noise and non-linear dynamics
- Period Comparison: Second half mean (21.92) exceeded first half (18.02) by 21.7%, confirming acceleration in later years
- Volatility: Standard deviation of 6.74 and range of 4–50 show considerable daily fluctuation despite upward trajectory
Interpretation
The metric demonstrates genuine upward momentum over the 5-year window, with the second half substantially outperforming the first. However, the weak R² signals that daily values are driven by factors beyond a simple linear progression—likely including seasonal patterns, external shocks, or operational variability. The 77% cumulative gain is meaningful, but
Trend Analysis
Time Series with Trend Overlay
Trend Analysis
Time Series with Trend Overlay
Time series trend visualization with linear or loess trend overlay
Key Insights
Trend Analysis
Purpose
This section identifies the directional movement of the metric over the 5-year observation period (2013–2017). Understanding trend direction answers whether the underlying phenomenon is growing, declining, or stable, which is foundational for assessing long-term performance and detecting structural shifts in the data.
Key Findings
- Trend Direction: Increasing - The metric shows upward movement from 2013 to 2017, with the trend line rising from 16.2 to 23.75
- Linear Slope: 0.004 - Each day contributes a modest 0.004-unit increase on average
- R-squared (0.105): Weak trend strength - Only 10.5% of variance is explained by the linear trend, indicating substantial daily fluctuation around the underlying pattern
- Pattern Observed: Steady but noisy upward trajectory with high volatility relative to trend magnitude
Interpretation
The data exhibits a genuine increasing trend, but the weak R-squared reveals that daily values deviate significantly from the fitted line. The trend line climbs 7.55 units over 1,826 days (76.9% total change), yet individual observations range from 4 to 50, suggesting external factors, seasonal patterns, or measurement variability dominate short-term behavior. The linear model captures the long-term direction but mis
Key Insights
Trend Analysis
Purpose
This section identifies the directional movement of the metric over the 5-year observation period (2013–2017). Understanding trend direction answers whether the underlying phenomenon is growing, declining, or stable, which is foundational for assessing long-term performance and detecting structural shifts in the data.
Key Findings
- Trend Direction: Increasing - The metric shows upward movement from 2013 to 2017, with the trend line rising from 16.2 to 23.75
- Linear Slope: 0.004 - Each day contributes a modest 0.004-unit increase on average
- R-squared (0.105): Weak trend strength - Only 10.5% of variance is explained by the linear trend, indicating substantial daily fluctuation around the underlying pattern
- Pattern Observed: Steady but noisy upward trajectory with high volatility relative to trend magnitude
Interpretation
The data exhibits a genuine increasing trend, but the weak R-squared reveals that daily values deviate significantly from the fitted line. The trend line climbs 7.55 units over 1,826 days (76.9% total change), yet individual observations range from 4 to 50, suggesting external factors, seasonal patterns, or measurement variability dominate short-term behavior. The linear model captures the long-term direction but mis
Rolling Average
Smoothed Time Series Filtering Noise
Rolling Average
Smoothed Time Series
Smoothed time series using rolling average to filter noise
Key Insights
Rolling Average
Purpose
The rolling 7-day average isolates the underlying trend by smoothing daily volatility across the 2013–2017 period. This technique reveals directional momentum and regime shifts that raw daily values obscure, enabling clearer identification of sustained changes versus temporary fluctuations in the metric.
Key Findings
- Rolling Window Size: 7 days—balances noise reduction with responsiveness to genuine trend changes
- Rolling Average Range: 8.43 to 32.57 (vs. raw range of 4–50)—demonstrates substantial volatility dampening while preserving trend structure
- Mean Smoothed Value: 19.98—nearly identical to raw mean (19.97), confirming the filter preserves central tendency
- Reduced Volatility: Standard deviation drops from 6.74 (raw) to 4.64 (smoothed)—31% noise reduction
- Data Completeness: 0.3% missing values (first 6 observations lack sufficient history for 7-day window)
Interpretation
The smoothed series confirms the underlying linear upward trend identified in the overall analysis while filtering short-term noise. The convergence of smoothed and raw means validates that the trend is genuine rather than an artifact of aggregation. The reduced standard deviation (4.64 vs. 6.74) demonstrates
Key Insights
Rolling Average
Purpose
The rolling 7-day average isolates the underlying trend by smoothing daily volatility across the 2013–2017 period. This technique reveals directional momentum and regime shifts that raw daily values obscure, enabling clearer identification of sustained changes versus temporary fluctuations in the metric.
Key Findings
- Rolling Window Size: 7 days—balances noise reduction with responsiveness to genuine trend changes
- Rolling Average Range: 8.43 to 32.57 (vs. raw range of 4–50)—demonstrates substantial volatility dampening while preserving trend structure
- Mean Smoothed Value: 19.98—nearly identical to raw mean (19.97), confirming the filter preserves central tendency
- Reduced Volatility: Standard deviation drops from 6.74 (raw) to 4.64 (smoothed)—31% noise reduction
- Data Completeness: 0.3% missing values (first 6 observations lack sufficient history for 7-day window)
Interpretation
The smoothed series confirms the underlying linear upward trend identified in the overall analysis while filtering short-term noise. The convergence of smoothed and raw means validates that the trend is genuine rather than an artifact of aggregation. The reduced standard deviation (4.64 vs. 6.74) demonstrates
Period Comparison
First Half vs Second Half Statistical Comparison
Period Comparison
First Half vs Second Half
First half vs second half period-over-period comparison
Key Insights
Period Comparison
Purpose
This section compares performance across the 2013-2017 observation period by splitting the timeline at its midpoint. It reveals whether the metric showed sustained improvement, decline, or stability over time, helping identify if meaningful shifts occurred during the five-year window.
Key Findings
- First Half Mean (2013-mid 2015): 18.02 - Establishes the baseline performance level for the initial portion of the observation period
- Second Half Mean (mid 2015-2017): 21.92 - Represents a notable increase from the first half baseline
- Period-over-Period Change: +21.7% - Indicates substantial upward movement between the two halves, with the second half averaging 3.9 points higher
Interpretation
The 21.7% increase from first to second half demonstrates a clear upward trajectory in the metric over the five-year span. Both periods maintained similar minimum values (4), but the second half achieved a higher maximum (50 vs. 43) and elevated median (22 vs. 18), suggesting the improvement was broad-based rather than driven by isolated outliers. The slightly higher variability in the second half (std_dev: 6.85 vs. 6.03) indicates more volatility accompanied this growth.
Context
This comparison aligns with the overall
Key Insights
Period Comparison
Purpose
This section compares performance across the 2013-2017 observation period by splitting the timeline at its midpoint. It reveals whether the metric showed sustained improvement, decline, or stability over time, helping identify if meaningful shifts occurred during the five-year window.
Key Findings
- First Half Mean (2013-mid 2015): 18.02 - Establishes the baseline performance level for the initial portion of the observation period
- Second Half Mean (mid 2015-2017): 21.92 - Represents a notable increase from the first half baseline
- Period-over-Period Change: +21.7% - Indicates substantial upward movement between the two halves, with the second half averaging 3.9 points higher
Interpretation
The 21.7% increase from first to second half demonstrates a clear upward trajectory in the metric over the five-year span. Both periods maintained similar minimum values (4), but the second half achieved a higher maximum (50 vs. 43) and elevated median (22 vs. 18), suggesting the improvement was broad-based rather than driven by isolated outliers. The slightly higher variability in the second half (std_dev: 6.85 vs. 6.03) indicates more volatility accompanied this growth.
Context
This comparison aligns with the overall
Value Distribution
Histogram Showing Value Spread and Shape
Distribution
Value Frequency Histogram
Value distribution histogram showing spread and shape
Key Insights
Distribution
Purpose
This section characterizes the overall spread and central tendency of values independent of time, answering what typical values look like and how much variability exists. Understanding the distribution shape is essential for assessing data quality and identifying whether the observed upward trend (from the overall analysis) represents a meaningful shift relative to the natural variability in the metric.
Key Findings
- Mean vs. Median: Mean of 19.97 exceeds median of 19.00, indicating right-skewed distribution with high-value outliers
- Variability: Standard deviation of 6.74 represents moderate spread around the mean (approximately ±34% relative to mean)
- Range: Values span from 4 to 50, a 46-unit range with peak frequency concentrated in the 11–13 bin (165 observations)
- Distribution Shape: Right-skewed pattern shows most observations cluster in lower-to-mid range, with sparse high values pulling the mean upward
Interpretation
The data exhibits moderate variability with a slight right skew, meaning typical values hover near 19 but occasional high values (up to 50) inflate the mean. This distribution context is critical for evaluating the observed linear trend: the 10-unit increase from 13 to 23 over five years represents movement within the natural variability range, though the trend direction aligns
Key Insights
Distribution
Purpose
This section characterizes the overall spread and central tendency of values independent of time, answering what typical values look like and how much variability exists. Understanding the distribution shape is essential for assessing data quality and identifying whether the observed upward trend (from the overall analysis) represents a meaningful shift relative to the natural variability in the metric.
Key Findings
- Mean vs. Median: Mean of 19.97 exceeds median of 19.00, indicating right-skewed distribution with high-value outliers
- Variability: Standard deviation of 6.74 represents moderate spread around the mean (approximately ±34% relative to mean)
- Range: Values span from 4 to 50, a 46-unit range with peak frequency concentrated in the 11–13 bin (165 observations)
- Distribution Shape: Right-skewed pattern shows most observations cluster in lower-to-mid range, with sparse high values pulling the mean upward
Interpretation
The data exhibits moderate variability with a slight right skew, meaning typical values hover near 19 but occasional high values (up to 50) inflate the mean. This distribution context is critical for evaluating the observed linear trend: the 10-unit increase from 13 to 23 over five years represents movement within the natural variability range, though the trend direction aligns
Summary Table
Aggregated Statistics by Time Period
Summary Table
Aggregated Statistics by Period
Aggregated statistics by time period (daily, weekly, or monthly)
| period | mean_val | median_val | min_val | max_val | count | total |
|---|---|---|---|---|---|---|
| 2013-01-01 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-01-02 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-01-03 | 14.000 | 14.000 | 14.000 | 14.000 | 1.000 | 14.000 |
| 2013-01-04 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-01-05 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-01-06 | 12.000 | 12.000 | 12.000 | 12.000 | 1.000 | 12.000 |
| 2013-01-07 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-01-08 | 9.000 | 9.000 | 9.000 | 9.000 | 1.000 | 9.000 |
| 2013-01-09 | 12.000 | 12.000 | 12.000 | 12.000 | 1.000 | 12.000 |
| 2013-01-10 | 9.000 | 9.000 | 9.000 | 9.000 | 1.000 | 9.000 |
| 2013-01-11 | 9.000 | 9.000 | 9.000 | 9.000 | 1.000 | 9.000 |
| 2013-01-12 | 7.000 | 7.000 | 7.000 | 7.000 | 1.000 | 7.000 |
| 2013-01-13 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-01-14 | 12.000 | 12.000 | 12.000 | 12.000 | 1.000 | 12.000 |
| 2013-01-15 | 5.000 | 5.000 | 5.000 | 5.000 | 1.000 | 5.000 |
| 2013-01-16 | 7.000 | 7.000 | 7.000 | 7.000 | 1.000 | 7.000 |
| 2013-01-17 | 16.000 | 16.000 | 16.000 | 16.000 | 1.000 | 16.000 |
| 2013-01-18 | 7.000 | 7.000 | 7.000 | 7.000 | 1.000 | 7.000 |
| 2013-01-19 | 18.000 | 18.000 | 18.000 | 18.000 | 1.000 | 18.000 |
| 2013-01-20 | 15.000 | 15.000 | 15.000 | 15.000 | 1.000 | 15.000 |
| 2013-01-21 | 8.000 | 8.000 | 8.000 | 8.000 | 1.000 | 8.000 |
| 2013-01-22 | 7.000 | 7.000 | 7.000 | 7.000 | 1.000 | 7.000 |
| 2013-01-23 | 9.000 | 9.000 | 9.000 | 9.000 | 1.000 | 9.000 |
| 2013-01-24 | 8.000 | 8.000 | 8.000 | 8.000 | 1.000 | 8.000 |
| 2013-01-25 | 14.000 | 14.000 | 14.000 | 14.000 | 1.000 | 14.000 |
| 2013-01-26 | 12.000 | 12.000 | 12.000 | 12.000 | 1.000 | 12.000 |
| 2013-01-27 | 12.000 | 12.000 | 12.000 | 12.000 | 1.000 | 12.000 |
| 2013-01-28 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-01-29 | 6.000 | 6.000 | 6.000 | 6.000 | 1.000 | 6.000 |
| 2013-01-30 | 9.000 | 9.000 | 9.000 | 9.000 | 1.000 | 9.000 |
| 2013-01-31 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-02-01 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-02-02 | 21.000 | 21.000 | 21.000 | 21.000 | 1.000 | 21.000 |
| 2013-02-03 | 15.000 | 15.000 | 15.000 | 15.000 | 1.000 | 15.000 |
| 2013-02-04 | 14.000 | 14.000 | 14.000 | 14.000 | 1.000 | 14.000 |
| 2013-02-05 | 9.000 | 9.000 | 9.000 | 9.000 | 1.000 | 9.000 |
| 2013-02-06 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-02-07 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-02-08 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-02-09 | 14.000 | 14.000 | 14.000 | 14.000 | 1.000 | 14.000 |
| 2013-02-10 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-02-11 | 16.000 | 16.000 | 16.000 | 16.000 | 1.000 | 16.000 |
| 2013-02-12 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-02-13 | 14.000 | 14.000 | 14.000 | 14.000 | 1.000 | 14.000 |
| 2013-02-14 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-02-15 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-02-16 | 7.000 | 7.000 | 7.000 | 7.000 | 1.000 | 7.000 |
| 2013-02-17 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-02-18 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-02-19 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-02-20 | 7.000 | 7.000 | 7.000 | 7.000 | 1.000 | 7.000 |
| 2013-02-21 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-02-22 | 12.000 | 12.000 | 12.000 | 12.000 | 1.000 | 12.000 |
| 2013-02-23 | 15.000 | 15.000 | 15.000 | 15.000 | 1.000 | 15.000 |
| 2013-02-24 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-02-25 | 7.000 | 7.000 | 7.000 | 7.000 | 1.000 | 7.000 |
| 2013-02-26 | 9.000 | 9.000 | 9.000 | 9.000 | 1.000 | 9.000 |
| 2013-02-27 | 9.000 | 9.000 | 9.000 | 9.000 | 1.000 | 9.000 |
| 2013-02-28 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-03-01 | 15.000 | 15.000 | 15.000 | 15.000 | 1.000 | 15.000 |
| 2013-03-02 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-03-03 | 20.000 | 20.000 | 20.000 | 20.000 | 1.000 | 20.000 |
| 2013-03-04 | 14.000 | 14.000 | 14.000 | 14.000 | 1.000 | 14.000 |
| 2013-03-05 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-03-06 | 17.000 | 17.000 | 17.000 | 17.000 | 1.000 | 17.000 |
| 2013-03-07 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-03-08 | 15.000 | 15.000 | 15.000 | 15.000 | 1.000 | 15.000 |
| 2013-03-09 | 16.000 | 16.000 | 16.000 | 16.000 | 1.000 | 16.000 |
| 2013-03-10 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-03-11 | 18.000 | 18.000 | 18.000 | 18.000 | 1.000 | 18.000 |
| 2013-03-12 | 14.000 | 14.000 | 14.000 | 14.000 | 1.000 | 14.000 |
| 2013-03-13 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-03-14 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-03-15 | 14.000 | 14.000 | 14.000 | 14.000 | 1.000 | 14.000 |
| 2013-03-16 | 10.000 | 10.000 | 10.000 | 10.000 | 1.000 | 10.000 |
| 2013-03-17 | 22.000 | 22.000 | 22.000 | 22.000 | 1.000 | 22.000 |
| 2013-03-18 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-03-19 | 19.000 | 19.000 | 19.000 | 19.000 | 1.000 | 19.000 |
| 2013-03-20 | 14.000 | 14.000 | 14.000 | 14.000 | 1.000 | 14.000 |
| 2013-03-21 | 17.000 | 17.000 | 17.000 | 17.000 | 1.000 | 17.000 |
| 2013-03-22 | 21.000 | 21.000 | 21.000 | 21.000 | 1.000 | 21.000 |
| 2013-03-23 | 21.000 | 21.000 | 21.000 | 21.000 | 1.000 | 21.000 |
| 2013-03-24 | 19.000 | 19.000 | 19.000 | 19.000 | 1.000 | 19.000 |
| 2013-03-25 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-03-26 | 16.000 | 16.000 | 16.000 | 16.000 | 1.000 | 16.000 |
| 2013-03-27 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-03-28 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-03-29 | 17.000 | 17.000 | 17.000 | 17.000 | 1.000 | 17.000 |
| 2013-03-30 | 19.000 | 19.000 | 19.000 | 19.000 | 1.000 | 19.000 |
| 2013-03-31 | 20.000 | 20.000 | 20.000 | 20.000 | 1.000 | 20.000 |
| 2013-04-01 | 11.000 | 11.000 | 11.000 | 11.000 | 1.000 | 11.000 |
| 2013-04-02 | 19.000 | 19.000 | 19.000 | 19.000 | 1.000 | 19.000 |
| 2013-04-03 | 24.000 | 24.000 | 24.000 | 24.000 | 1.000 | 24.000 |
| 2013-04-04 | 18.000 | 18.000 | 18.000 | 18.000 | 1.000 | 18.000 |
| 2013-04-05 | 19.000 | 19.000 | 19.000 | 19.000 | 1.000 | 19.000 |
| 2013-04-06 | 23.000 | 23.000 | 23.000 | 23.000 | 1.000 | 23.000 |
| 2013-04-07 | 17.000 | 17.000 | 17.000 | 17.000 | 1.000 | 17.000 |
| 2013-04-08 | 19.000 | 19.000 | 19.000 | 19.000 | 1.000 | 19.000 |
| 2013-04-09 | 13.000 | 13.000 | 13.000 | 13.000 | 1.000 | 13.000 |
| 2013-04-10 | 19.000 | 19.000 | 19.000 | 19.000 | 1.000 | 19.000 |
Key Insights
Summary Table
Purpose
This section provides daily-level aggregated statistics across the 5-year observation period (2013–2017), enabling period-by-period comparison of central tendency and range. It answers what the typical value and variability look like for each individual day, supporting identification of anomalies, outliers, and temporal patterns that may inform broader trend analysis.
Key Findings
- Daily Granularity: Each of the 1,826 rows represents a single calendar day with one observation per day (count = 1), making mean, median, min, and max identical for each period.
- Value Range: Daily values span from 4 to 50, with an overall mean of 19.97 and median of 19, indicating a slightly right-skewed distribution (skew = 0.43).
- Consistency: Standard deviation of 6.74 across all days reflects moderate daily variability around the central tendency.
- No Aggregation Needed: Since each day contains exactly one observation, this table essentially mirrors the raw daily time series without smoothing or compression.
Interpretation
The summary table confirms that the underlying data is already at daily resolution with single values per day. The identical mean/median/min/max values within each row reflect this one-to-one mapping. The 76.9% increase from first value (13) to last
Key Insights
Summary Table
Purpose
This section provides daily-level aggregated statistics across the 5-year observation period (2013–2017), enabling period-by-period comparison of central tendency and range. It answers what the typical value and variability look like for each individual day, supporting identification of anomalies, outliers, and temporal patterns that may inform broader trend analysis.
Key Findings
- Daily Granularity: Each of the 1,826 rows represents a single calendar day with one observation per day (count = 1), making mean, median, min, and max identical for each period.
- Value Range: Daily values span from 4 to 50, with an overall mean of 19.97 and median of 19, indicating a slightly right-skewed distribution (skew = 0.43).
- Consistency: Standard deviation of 6.74 across all days reflects moderate daily variability around the central tendency.
- No Aggregation Needed: Since each day contains exactly one observation, this table essentially mirrors the raw daily time series without smoothing or compression.
Interpretation
The summary table confirms that the underlying data is already at daily resolution with single values per day. The identical mean/median/min/max values within each row reflect this one-to-one mapping. The 76.9% increase from first value (13) to last
Overall Statistics
Summary Metrics for Entire Time Series
Overall Statistics
Summary Metrics
Overall time series summary metrics
Overall metrics
| Metric | Value |
|---|---|
| Observations | 1826 |
| Date Range | 2013-01-01 to 2017-12-31 |
| First Value | 13.00 |
| Last Value | 23.00 |
| Total Change | +10.00 |
| Percent Change | +76.9% |
Key Insights
Overall Statistics
Purpose
This section establishes the baseline trajectory of the time series by examining overall change magnitude and direction. Understanding whether a dataset exhibits substantial shifts is critical for identifying whether underlying processes have fundamentally changed, which informs all subsequent trend and distribution analyses.
Key Findings
- Observation Count: 1,826 daily measurements provide robust statistical power across the 5-year period
- Total Change: +10 units (76.9% increase) from first to last value represents a substantial directional shift
- Trend Direction: Values increased from 13 to 23, indicating consistent upward movement over the observation window
- Mean Value: 19.97 with median of 19 suggests the typical daily value clusters near the midpoint, with moderate variability (SD: 6.74)
Interpretation
The 77% increase from 2013 to 2017 signals a meaningful upward trend in the measured phenomenon. The mean value of ~20 indicates that while the series started below average (13), it ended above average (23), reflecting genuine growth rather than random fluctuation. The moderate standard deviation relative to the range (4–50) reveals consistent daily volatility alongside the broader upward trajectory, suggesting the underlying process contains both systematic growth and regular noise.
Context
This overview captures endpoint-to-endpoint change but masks intra-period volatility and potential
Key Insights
Overall Statistics
Purpose
This section establishes the baseline trajectory of the time series by examining overall change magnitude and direction. Understanding whether a dataset exhibits substantial shifts is critical for identifying whether underlying processes have fundamentally changed, which informs all subsequent trend and distribution analyses.
Key Findings
- Observation Count: 1,826 daily measurements provide robust statistical power across the 5-year period
- Total Change: +10 units (76.9% increase) from first to last value represents a substantial directional shift
- Trend Direction: Values increased from 13 to 23, indicating consistent upward movement over the observation window
- Mean Value: 19.97 with median of 19 suggests the typical daily value clusters near the midpoint, with moderate variability (SD: 6.74)
Interpretation
The 77% increase from 2013 to 2017 signals a meaningful upward trend in the measured phenomenon. The mean value of ~20 indicates that while the series started below average (13), it ended above average (23), reflecting genuine growth rather than random fluctuation. The moderate standard deviation relative to the range (4–50) reveals consistent daily volatility alongside the broader upward trajectory, suggesting the underlying process contains both systematic growth and regular noise.
Context
This overview captures endpoint-to-endpoint change but masks intra-period volatility and potential