--- a/pkg/controllers/bottlenecks.go	Fri Sep 06 16:17:36 2019 +0200
+++ b/pkg/controllers/bottlenecks.go	Fri Sep 06 17:22:42 2019 +0200
@@ -10,6 +10,7 @@
 //
 // Author(s):
 //  * Sascha L. Teichmann <sascha.teichmann@intevation.de>
+//  * Sascha Wilde <wilde@intevation.de>
 
 package controllers
 
@@ -131,6 +132,42 @@
 	}
 }
 
+// According to clarification, it has to be assumed, that at times
+// with no data, the best case (which by convention is the highest
+// class created by classify()) should be assumed.  That is due to the
+// fact, that at times where bottlenecks are ot a limiting factor on
+// the waterway, services don't brovide any data for the bottleneck in
+// question.
+//
+// FIXME: A potentional improvement could be to intercest the time
+// ranges with the time ranges where bottlenecks were "active" (this
+// _might_ be derivable fromt the validity periods in the bottleneck
+// data.  So it _might_ be possible do detect actual missing data (BN
+// valid, but no data from FA service).  Anyway, this is left out for
+// now, as many clarification regarding the base assumtions would bee
+// needed and the results still might be unrelyable.
+func optimisticPadClassification(
+	from, to time.Time,
+	classified []time.Duration,
+) []time.Duration {
+	var actualDuration time.Duration
+	maxDuration := to.Sub(from)
+
+	for i := 0; i < len(classified); i++ {
+		actualDuration += classified[i]
+	}
+
+	delta := maxDuration - actualDuration
+	if delta > 0 {
+		log.Printf("info: time interval: (%v - %v)\n", from, to)
+		log.Printf("info: found only data for %.2f hours, padding by %.2f hours\n",
+			actualDuration.Hours(), delta.Hours())
+		classified[len(classified)-1] = classified[len(classified)-1] + delta
+	}
+	
+	return classified
+}
+
 func (measurements availMeasurements) classify(
 	from, to time.Time,
 	breaks []float64,
@@ -158,7 +195,7 @@
 	})
 
 	if idx >= len(measurements) {
-		return result
+		return optimisticPadClassification(from, to, result)
 	}
 
 	// Be safe for interpolation.
@@ -173,7 +210,7 @@
 		p2 := &measurements[i+1]
 
 		if p1.when.After(to) {
-			return result
+			return optimisticPadClassification(from, to, result)
 		}
 
 		if p2.when.Before(from) {
@@ -214,7 +251,7 @@
 		}
 	}
 
-	return result
+	return optimisticPadClassification(from, to, result)
 }
 
 func orderTime(a, b time.Time) (time.Time, time.Time) {
@@ -482,7 +519,13 @@
 
 	interval := intervals[mode](from, to)
 
+	now := time.Now()
 	for pfrom, pto, label := interval(); label != ""; pfrom, pto, label = interval() {
+		// Don't interpolate for the future
+		if now.Sub(pto) < 0 {
+			pto = now
+		}
+
 		lnwl := ms.classify(
 			pfrom, pto,
 			ldcRefs,
@@ -638,7 +681,12 @@
 
 	interval := intervals[mode](from, to)
 
+	now := time.Now()
 	for pfrom, pto, label := interval(); label != ""; pfrom, pto, label = interval() {
+		// Don't interpolate for the future
+		if now.Sub(pto) < 0 {
+			pto = now
+		}
 
 		ldc := ms.classify(
 			pfrom, pto,

--- a/pkg/controllers/stretches.go	Fri Sep 06 16:17:36 2019 +0200
+++ b/pkg/controllers/stretches.go	Fri Sep 06 17:22:42 2019 +0200
@@ -279,7 +279,13 @@
 
 				var ldc, breaks []time.Duration
 
+				now := time.Now()
 				for _, bn := range loaded {
+					// Don't interpolate for the future
+					if now.Sub(res.to) < 0 {
+						res.to = now
+					}
+
 					l := bn.measurements.classify(
 						res.from, res.to,
 						bn.ldc,
@@ -539,7 +545,13 @@
 			for res := range jobCh {
 				var ldc, breaks []time.Duration
 
+				now := time.Now()
 				for _, bn := range loaded {
+					// Don't interpolate for the future
+					if now.Sub(res.to) < 0 {
+						res.to = now
+					}
+
 					l := bn.measurements.classify(
 						res.from, res.to,
 						bn.ldc,