diff --git a/analytics/lib/fixtures.py b/analytics/lib/fixtures.py
index 2fe562c250..c7dc4909dd 100644
--- a/analytics/lib/fixtures.py
+++ b/analytics/lib/fixtures.py
@@ -11,14 +11,15 @@ from random import gauss, random, seed
 
 from six.moves import range, zip
 
-def generate_time_series_data(length, business_hours_base, non_business_hours_base,
+def generate_time_series_data(days=100, business_hours_base=10, non_business_hours_base=10,
                               growth=1, autocorrelation=0, spikiness=1, holiday_rate=0,
-                              frequency=CountStat.HOUR, is_gauge=False):
+                              frequency=CountStat.DAY, is_gauge=False):
     # type: (int, float, float, float, float, float, float, str, bool) -> List[int]
     """
     Generate semi-realistic looking time series data for testing analytics graphs.
 
-    length -- Number of data points returned.
+    days -- Number of days of data. Is the number of data points generated if
+        frequency is CountStat.DAY.
     business_hours_base -- Average value during a business hour (or day) at beginning of
         time series, if frequency is CountStat.HOUR (CountStat.DAY, respectively).
     non_business_hours_base -- The above, for non-business hours/days.
@@ -28,21 +29,29 @@ def generate_time_series_data(length, business_hours_base, non_business_hours_ba
         function of the previous point.
     spikiness -- 0 means no randomness (other than holiday_rate), higher values increase
         the variance.
-    holiday_rate -- Fraction of points randomly set to 0.
+    holiday_rate -- Fraction of days randomly set to 0, largely for testing how we handle 0s.
     frequency -- Should be CountStat.HOUR or CountStat.DAY.
     is_gauge -- If True, return partial sum of the series.
     """
-    if length < 2:
-        raise ValueError("length must be at least 2")
     if frequency == CountStat.HOUR:
+        length = days*24
         seasonality = [non_business_hours_base] * 24 * 7
         for day in range(5):
             for hour in range(8):
                 seasonality[24*day + hour] = business_hours_base
+        holidays  = []
+        for i in range(days):
+            holidays.extend([random() < holiday_rate] * 24)
     elif frequency == CountStat.DAY:
-        seasonality = [business_hours_base]*5 + [non_business_hours_base]*2
+        length = days
+        seasonality = [8*business_hours_base + 16*non_business_hours_base] * 5 + \
+                      [24*non_business_hours_base] * 2
+        holidays = [random() < holiday_rate for i in range(days)]
     else:
         raise ValueError("Unknown frequency: %s" % (frequency,))
+    if length < 2:
+        raise ValueError("Must be generating at least 2 data points. "
+                         "Currently generating %s" % (length,))
     growth_base = growth ** (1. / (length-1))
     values_no_noise = [seasonality[i % len(seasonality)] * (growth_base**i) for i in range(length)]
 
@@ -51,8 +60,8 @@ def generate_time_series_data(length, business_hours_base, non_business_hours_ba
     for i in range(1, length):
         noise_scalars.append(noise_scalars[-1]*autocorrelation + gauss(0, 1)*(1-autocorrelation))
 
-    values = [0 if random() < holiday_rate else int(v + sqrt(v)*noise_scalar*spikiness)
-              for v, noise_scalar in zip(values_no_noise, noise_scalars)]
+    values = [0 if holiday else int(v + sqrt(v)*noise_scalar*spikiness)
+              for v, noise_scalar, holiday in zip(values_no_noise, noise_scalars, holidays)]
     if is_gauge:
         for i in range(1, length):
             values[i] = values[i-1] + values[i]
diff --git a/analytics/management/commands/populate_analytics_db.py b/analytics/management/commands/populate_analytics_db.py
index d5958dfc6a..74c755bcad 100644
--- a/analytics/management/commands/populate_analytics_db.py
+++ b/analytics/management/commands/populate_analytics_db.py
@@ -48,9 +48,11 @@ class Command(BaseCommand):
         stat = COUNT_STATS['active_users:is_bot']
         if not RealmCount.objects.filter(property=stat.property).exists():
             last_end_time = floor_to_day(timezone.now())
-            human_data = generate_time_series_data(100, 30, 10, growth=5, autocorrelation=.5,
+            human_data = generate_time_series_data(days=100, business_hours_base=30,
+                                                   non_business_hours_base=10, growth=5, autocorrelation=.5,
                                                    spikiness=3, frequency=CountStat.DAY)
-            bot_data = generate_time_series_data(100, 20, 20, growth=3, frequency=CountStat.DAY)
+            bot_data = generate_time_series_data(days=100, business_hours_base=20,
+                                                 non_business_hours_base=20, growth=3, frequency=CountStat.DAY)
             bulk_create_realmcount(stat.property, 'false', last_end_time,
                                    stat.frequency, stat.interval, human_data, realm)
             bulk_create_realmcount(stat.property, 'true', last_end_time,