diff --git a/its.py b/its.py
new file mode 100644
index 0000000..8f70bb2
--- /dev/null
+++ b/its.py
@@ -0,0 +1,121 @@
+import os
+import os
+import sys
+from datetime import datetime
+from datetime import timedelta
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+from common import calc_intervals, imprt, printnoln, rprint, DAYS_NEW_USER
+from loader import load, dmt, cms
+
+OLD_USER_PERCENTILE = 0.95
+
+colors = ['red', 'green', 'blue', 'orange', 'deeppink']
+
+
+def main(folder, intervl):
+    users, posts, firstcontrib, sumcontrib = load(folder)
+
+    intervals = calc_intervals(posts, intervl)
+
+    start = cms()
+    printnoln("reading sentiments ...")
+    cachedsentiments = imprt(folder + "/output/sentiments.py").answers
+    rprint("reading sentiments ... took " + str(cms() - start) + "ms")
+
+    outputdir = folder + "/output/its/"
+    os.system("mkdir -p " + outputdir)
+
+    data = []
+    for (option_date_from, option_date_to) in intervals:
+        if option_date_to <= datetime.fromisoformat("2015-01-01T00:00:00"):
+            data.append(float("nan"))
+            continue
+        print(option_date_from.strftime("%d-%m-%Y") + " to " + option_date_to.strftime("%d-%m-%Y"))
+        # avg sentiments
+        # print(dmt(posts).map(lambda p: [cachedsentiments[a['Id']]['compound']
+        #                                 for a in p['Answers'] if option_date_from <= a['CreationDate'] < option_date_to
+        #                                 and firstcontrib[p['OwnerUserId']] + timedelta(days=DAYS_NEW_USER) <= a['CreationDate']])
+        #       .filter(lambda p: p != [])
+        #       .getresults())
+        # break
+        filtered = (dmt(posts).map(lambda p: [cachedsentiments[a['Id']]['compound']
+                                              for a in p['Answers'] if option_date_from <= a['CreationDate'] < option_date_to
+                                              and firstcontrib[p['OwnerUserId']] + timedelta(days=DAYS_NEW_USER) <= a['CreationDate']])
+                    .filter(lambda p: p != [])
+                    .reduce(lambda a, b: a + b, lambda a, b: a + b, lambda: [])
+                    .getresults())
+        avg = np.average(filtered) if len(filtered) > 0 else float("nan")
+        data.append(avg)
+
+    # filter nan entries
+    for i in range(len(data)):
+        while i < len(data) and str(data[i]) == "nan":
+            del data[i]
+            del intervals[i]
+
+    print("Computing ITS ...")
+    t = np.reshape(np.array([i for i in range(len(data))]), (-1, 1))
+    # print("t", t)
+    x = np.reshape(np.array([(0 if option_date_to <= datetime.fromisoformat("2018-09-01T00:00:00") else 1) for (option_date_from, option_date_to) in intervals]), (-1, 1))
+    # print("x", x)
+    X = np.reshape(np.array([data[0] for i in range(len(data))]), (-1, 1))
+    # print("X", X)
+    X = np.concatenate((X, t), 1)
+    X = np.concatenate((X, x), 1)
+    X = np.concatenate((X, np.multiply(t, x)), 1)
+    y = np.reshape(np.array(data), (-1, 1))
+    # print("Xfin", X)
+    # print("y", y)
+    reg = LinearRegression()
+    reg.fit(X, y)
+    score = reg.score(X, y);
+    coef = np.reshape(np.array(reg.coef_), (-1, 1))
+    its = X.dot(coef) + data[0]
+    print("score: " + str(score))
+    print("coef: " + str(coef))
+    print("its: " + str(its))
+
+    fig = plt.figure(figsize=(16, 12))
+    plt.plot([i[0] for i in intervals], data, label="average sentiment")
+    plt.plot([i[0] for i in intervals], its, label="ITS (score " + str(score) + ")")
+    plt.title("Average sentiments for new users")
+    plt.xticks(rotation=90)
+    plt.xlabel("months")
+    plt.ylabel("sentiment")
+    plt.legend(loc="upper right")
+    outfile = outputdir + "/average_sentiments.png"
+    plt.savefig(outfile, bbox_inches='tight')
+    plt.close(fig)
+
+
+if __name__ == "__main__":
+    # execute only if run as a script
+    usage = sys.argv[0] + " <folder>"
+    if len(sys.argv) < 2:
+        print(usage)
+        sys.exit(1)
+    folder = sys.argv[1]
+    if not os.path.isdir(folder):
+        print(folder + " is not a folder")
+        sys.exit(1)
+    interval = 3
+    if len(sys.argv) >= 3:
+        if sys.argv[2].startswith("-i"):
+            interval = sys.argv[2][2:]
+            try:
+                interval = int(interval)
+            except ValueError:
+                print("-i: int required")
+                sys.exit(1)
+            if interval < 1 or interval > 12:
+                print("-i: only 1 - 12")
+                sys.exit(1)
+        else:
+            print("unknown parameter: " + sys.argv[2])
+            sys.exit(1)
+
+    main(folder, interval)
diff --git a/notes b/notes
index 9235a04..2cd6114 100644
--- a/notes
+++ b/notes
@@ -6,3 +6,10 @@ wann war die abweichung größer als 1 std abweichung
 
 plot anzahl der posts pro user in zeitraum x
 
+
+
+-----
+interrupted time series
+3 monate vor änderung und nache der änderungen
+
+da gibts 2 varianten: sprung, oder 2 linien