Update README.md

README.md

@@ -68,10 +68,10 @@ The dataset is sourced from a proprietary staging environment of an observabilit
 ## Comparison with Other Benchmarks
 
 The BOOM Benchmark diverges significantly from traditional time series datasets, including those in the [GiftEval](https://huggingface.co/datasets/Salesforce/GiftEval) suite, when analyzed using 12 standard and custom diagnostic features computed on normalized series (see Figure 3). These features capture key temporal and distributional characteristics:
-	•	Spectral entropy (unpredictability),
-	•	Skewness and kurtosis (distribution shape),
-	•	Autocorrelation coefficients (temporal structure),
-	•	Unit root tests and transience scores (stationarity and burstiness).
+ - Spectral entropy (unpredictability),
+ - Skewness and kurtosis (distribution shape),
+ - Autocorrelation coefficients (temporal structure),
+ - Unit root tests and transience scores (stationarity and burstiness).
 
 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.