diff --git a/time_series/README.md b/time_series/README.md
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--- a/time_series/README.md
+++ b/time_series/README.md
@@ -17,7 +17,14 @@ The included documents are:
   and suitable for stochastic processes. This makes them attractive for
   processing high-frequency data in finance and other fields. Using a
   basic set of operators, we easily construct more powerful combined
-  operators which cover a wide set of typical applications. 
-
+  operators which cover a wide set of typical applications.
 
+* [:scroll:](https://github.com/papers-we-love/papers-we-love/blob/master/time_series/ts-asap.pdf)
+  [ASAP: Automatic Smoothing for Attention Prioritization in Streaming Time Series Visualization](http://futuredata.stanford.edu/asap/)  - Kexin Rong, Peter Bailis
 
+  Time Series smoothing method to better prioritize attention in time series
+  exploration and monitoring visualizations, smooth the time series as much as
+  possible to remove noise while still retaining large-scale structure. We
+  develop a new technique for automatically smoothing streaming time series
+  that adaptively optimizes this trade-off between noise reduction (i.e.,
+  variance) and outlier retention (i.e., kurtosis).
diff --git a/time_series/ts-asap.pdf b/time_series/ts-asap.pdf
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