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README.md

@@ -30,13 +30,11 @@ Composed of real-world metrics data collected from Datadog, a leading observabil
 Note: the metrics comprising BOOM were generated from internal monitoring of pre-production environments, and **do not** include any customer data. 
 
 ![Dataset](intro_figure_boom.png)
-*<center>Figure 1: (a) Boom is comprised of observability time series data with distinct semantic categories corresponding to various temporal patterns; percentages indicate proportion of each category in Boom.
-   (b) 2D PCA projections of statistical features from [GiftEval](https://huggingface.co/datasets/Salesforce/GiftEval) and Boom highlight a clear distinction in the underlying time series characteristics of these two benchmarks.
-   (c) Boom is comprised of data from various system domains. </center>*
+*<center>Figure 1: A- BOOM consists of data from various domains. B- Example series from three of the domains. From left to right, these series represent: sum of failed requests on a backend API, grouped by error type and source (Application); CPU limits on a multi-tenant service deployed on a Kubernetes cluster, grouped by tenant (Infrastructure); and sum of command executions on a Redis cache, grouped by command (Database). </center>*
 
 
 Boom consists of approximately 350 million time-series points across 32,887 variates. The dataset is split into 2,807 individual time series with one or multiple variates. Each represents a metric query extracted from user-generated dashboards, notebooks, and monitors.
-These series vary widely in sampling frequency, temporal length, and number of variates. Looking beyond the basic characteristics of the series, we highlight a few of the typical challenging properties of observability time series (several of which are illustrated in Figure 1):
+These series vary widely in sampling frequency, temporal length, and number of variates. Looking beyond the basic characteristics of the series, we highlight a few of the typical challenging properties of observability time series (several of which are illustrated in Figure 2):
 
 - Zero-inflation: Many metrics track infrequent events (e.g., system errors), resulting in sparse series dominated by zeros with rare, informative spikes.
 
@@ -52,6 +50,9 @@ These series vary widely in sampling frequency, temporal length, and number of v
 
 - High cardinality: Observability data is often segmented by tags such as service, region, or instance, producing large families of multivariate series with high dimensionality but limited history per variate.
 
+![Dataset](series_examples.png)
+*<center>Figure 2: Examples of BOOM dataset showing the diversity of its series.</center>*
+
 ## Evaluating Models on BOOM
 
 We provide code with example evaluations of existing models; see the [code repository](https://github.com/DataDog/toto).
@@ -61,14 +62,14 @@ We provide code with example evaluations of existing models; see the [code repos
 Each entry in the dataset consists of:
 
 - A multivariate or univariate time series (one metric query with up to 100 variates)
-- Metadata including sampling start time, frequency, series length and variates number. Figure 2 shows the metadata decomposition of the dataset by number of series.
+- Metadata including sampling start time, frequency, series length and variates number. Figure 3 shows the metadata decomposition of the dataset by number of series.
 - Taxonomy labels for dataset stratification:
   - **Metric Type** (e.g., count, rate, gauge, histogram)
   - **Domain** (e.g., infrastructure, networking, security)
   - **Semantic Class** (e.g., skewed, seasonal, flat)
 
 ![Metadata](metadata.png)
-*<center>Figure 2: Representative figure showing the metadata breakdown by variate in the dataset: (left) sampling frequency distribution, (middle) series length distribution, and (right) number of variates distribution.</center>*
+*<center>Figure 3: Representative figure showing the metadata breakdown by variate in the dataset: (left) sampling frequency distribution, (middle) series length distribution, and (right) number of variates distribution.</center>*
 
 ## Collection and Sources
 
@@ -80,11 +81,12 @@ The BOOM Benchmark diverges significantly from traditional time series datasets,
  - Spectral entropy (unpredictability),
  - Skewness and kurtosis (distribution shape),
  - Autocorrelation coefficients (temporal structure),
- - Unit root tests and transience scores (stationarity and burstiness).
+ - Unit root tests (stationarity), 
+ - Flat spots (sparsity).
 
 
 ![Metadata](density_plots.png)
-*<center>Distributional comparison of 6 statistical features computed on normalized time series from the BOOM, GIFT-Eval, and LSF benchmark datasets. The broader and shifted distributions in the BOOM series reflect the increased diversity, irregularity, and nonstationarity characteristic of observability data.</center>*
+*<center>Figure 4: Distributional comparison of 6 statistical features computed on normalized time series from the BOOM, GIFT-Eval, and LSF benchmark datasets. The broader and shifted distributions in the BOOM series reflect the increased diversity, irregularity, and nonstationarity characteristic of observability data.</center>*
 
 
 BOOM series exhibit substantially higher spectral entropy, indicating greater irregularity in temporal dynamics. Distributions show heavier tails and more frequent structural breaks, as reflected by shifts in skewness and stationarity metrics. A wider range of transience scores highlights the presence of both persistent and highly volatile patterns—common in operational observability data but largely absent from curated academic datasets.