diff --git a/server/src/services/analytic_service/pattern_detector.rs b/server/src/services/analytic_service/pattern_detector.rs
index aae2c89..5549005 100644
--- a/server/src/services/analytic_service/pattern_detector.rs
+++ b/server/src/services/analytic_service/pattern_detector.rs
@@ -1,18 +1,23 @@
-use std::{thread, time};
-use tokio::time::{sleep, Duration};
-
 #[derive(Debug, Clone)]
 pub struct LearningResults {
-    backet_size: usize,
-    // avg_min: f64,
-    // avg_max: f64,
+    model: Vec<f64>, // avg_min: f64,
+                     // avg_max: f64
 }
 
+const CORR_THRESHOLD: f64 = 0.9;
+
 #[derive(Clone)]
 pub struct PatternDetector {
     learning_results: LearningResults,
 }
 
+fn nan_to_zero(n: f64) -> f64 {
+    if n.is_nan() {
+        return 0.;
+    }
+    return n;
+}
+
 // TODO: move this to loginc of analytic unit
 impl PatternDetector {
     pub fn new(learning_results: LearningResults) -> PatternDetector {
@@ -20,55 +25,88 @@ impl PatternDetector {
     }
 
     pub async fn learn(reads: &Vec<Vec<(u64, f64)>>) -> LearningResults {
-        // TODO: implement
-        let mut min_size = usize::MAX;
-        let mut max_size = 0usize;
-        for r in reads {
-            min_size = min_size.min(r.len());
-            max_size = max_size.max(r.len());
-        }
 
-        // let mut max_sum = 0;
-        // let mut min_sum = 0;
+        let size_avg = reads.iter().map(|r| r.len()).sum::<usize>() / reads.len();
 
-        // for read in reads {
-        //     let my_max = read.iter().map(|(t,v)| *v).max().unwrap();
-        //     let my_min = read.iter().min().unwrap();
-        // }
+        let mut stat = Vec::<(usize, f64)>::new();
+        for _i in 0..size_avg {
+            stat.push((0usize, 0f64));
+        }
 
-        LearningResults {
-            backet_size: (min_size + max_size) / 2,
+        for r in reads {
+            let xs: Vec<f64> = r.iter().map(|e| e.1).map(nan_to_zero).collect();
+            if xs.len() > size_avg {
+                let offset = (xs.len() - size_avg) / 2;
+                for i in 0..size_avg {
+                    stat[i].0 += 1;
+                    stat[i].1 += xs[i + offset];
+                }
+            } else {
+                let offset = (size_avg - xs.len()) / 2;
+                for i in 0..xs.len() {
+                    stat[i + offset].0 += 1;
+                    stat[i + offset].1 += xs[i];
+                }
+            }
         }
+
+        let model = stat.iter().map(|(c, v)| v / *c as f64).collect();
+
+        LearningResults { model }
     }
 
     // TODO: get iterator instead of vector
     pub fn detect(&self, ts: &Vec<(u64, f64)>) -> Vec<(u64, u64)> {
         let mut results = Vec::new();
         let mut i = 0;
-        while i < ts.len() - self.learning_results.backet_size {
-            let backet: Vec<_> = ts
-                .iter()
-                .skip(i)
-                .take(self.learning_results.backet_size)
-                .collect();
-
-            let mut min = f64::MAX;
-            let mut max = f64::MIN;
-
-            for (t, v) in backet.iter() {
-                min = v.min(min);
-                max = v.max(max);
+        let m = &self.learning_results.model;
+
+        // TODO: here we ignoring gaps in data
+        while i < ts.len() - self.learning_results.model.len() {
+            let mut backet = Vec::<f64>::new();
+
+            for j in 0..m.len() {
+                backet.push(nan_to_zero(ts[j + i].1));
             }
 
-            if min > 10_000. && max < 100_000. {
-                let from = backet[0].0;
-                let to = backet[backet.len() - 1].0;
+            let c = PatternDetector::corr(&backet, &m);
+
+            if c >= CORR_THRESHOLD {
+                let from = ts[i].0;
+                let to = ts[i + backet.len() - 1].0;
                 results.push((from, to));
             }
 
-            i += 100;
+            i += m.len();
         }
 
         return results;
     }
+
+    fn corr(xs: &Vec<f64>, ys: &Vec<f64>) -> f64 {
+
+        assert_eq!(xs.len(), ys.len());
+
+        let n = xs.len() as f64;
+        // TODO: compute it faster, with one iteration over x y
+        let s_xs: f64 = xs.iter().sum();
+        let s_ys: f64 = ys.iter().sum();
+        let s_xsys: f64 = xs.iter().zip(ys).map(|(xi, yi)| xi * yi).sum();
+        let s_xs_2: f64 = xs.iter().map(|xi| xi * xi).sum();
+        let s_ys_2: f64 = ys.iter().map(|yi| yi * yi).sum();
+
+        let numerator: f64 = n * s_xsys - s_xs * s_ys;
+        let denominator: f64 = ((n * s_xs_2 - s_xs * s_xs) * (n * s_ys_2 - s_ys * s_ys)).sqrt();
+
+        let result: f64 = numerator / denominator;
+
+        // assert!(result.abs() <= 1.01);
+
+        if result.abs() > 1.0 {
+            println!("WARNING: corr result > 1: {}", result);
+        }
+
+        return result;
+    }
+
 }