From 8de4e80a99ad4c6d2a225a087035bc728be19662 Mon Sep 17 00:00:00 2001
From: Anton Romanov <a.romanov@ulstu.ru>
Date: Fri, 28 Apr 2023 11:53:13 +0400
Subject: [PATCH] add code

---
 main.py | 116 ++++++++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 112 insertions(+), 4 deletions(-)

diff --git a/main.py b/main.py
index aafcad4..5506dd8 100644
--- a/main.py
+++ b/main.py
@@ -1,4 +1,112 @@
-i = 0
-if i == 0:
-    i = i + 1
-    print("This line will be printed. " + str(i))
+import matplotlib.pyplot as plt
+import pandas as pd
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
+import requests
+import json
+
+# ts = pd.read_csv("d:\\files\\Features count.csv")
+# ts.plot()
+# plt.grid()
+# plt.show()
+
+# moving_avg = ts.rolling(4).mean()
+# ts.plot()
+# plt.plot(moving_avg, color='green', label='Moving average')
+# plt.legend()
+# plt.grid()
+
+# model = ExponentialSmoothing(ts, trend="additive", seasonal="additive", seasonal_periods=5)
+# fit1 = model.fit()
+# pred1 = fit1.forecast(5)
+
+# plt.plot(ts, label='Features count')
+# plt.plot(pred1, color='red', label='Holt-Winters forecast')
+# plt.legend()
+# plt.grid()
+
+ts = pd.read_csv("d:\\files\\Testing time.csv")
+model = ExponentialSmoothing(ts, trend="additive", seasonal="additive", seasonal_periods=7)
+fit1 = model.fit()
+pred1 = fit1.forecast(10)
+
+plt.plot(ts, label='Testing time')
+plt.plot(pred1, color='red', label='Holt-Winters forecast')
+plt.legend()
+plt.grid()
+plt.show()
+
+# Использовать внешний сервис прогнозирования
+time_series = {
+    "originalTimeSeries": {
+        "values": [
+            {
+                "date": "2023-04-16T06:29:50.668476",
+                "value": 1
+            },
+            {
+                "date": "2023-04-17T06:29:50.668476",
+                "value": 2
+            },
+            {
+                "date": "2023-04-18T06:29:50.668476",
+                "value": 3
+            },
+            {
+                "date": "2023-04-19T06:29:50.668476",
+                "value": 4
+            },
+            {
+                "date": "2023-04-20T06:29:50.668476",
+                "value": 5
+            },
+            {
+                "date": "2023-04-21T06:29:50.668476",
+                "value": 6
+            },
+            {
+                "date": "2023-04-22T06:29:50.668476",
+                "value": 5
+            },
+            {
+                "date": "2023-04-23T06:29:50.668476",
+                "value": 4
+            },
+            {
+                "date": "2023-04-24T06:29:50.668476",
+                "value": 3
+            },
+            {
+                "date": "2023-04-25T06:29:50.668476",
+                "value": 2
+            },
+            {
+                "date": "2023-04-26T06:29:50.668476",
+                "value": 1
+            },
+            {
+                "date": "2023-04-27T06:29:50.668476",
+                "value": 2
+            }
+        ],
+        "name": 'test'
+    },
+    "countForecast": 5
+}
+
+source = []
+for v in time_series['originalTimeSeries']['values']:
+    source.append(v['value'])
+
+url = "http://time-series.athene.tech/api/1.0/getForecast"
+headers = {'Content-type': 'application/json'}
+r = requests.post(url, data=json.dumps(time_series), headers=headers)
+
+forecast = []
+for v in r.json()['timeSeries']['values']:
+    forecast.append(v['value'])
+forecast = list(filter(lambda a: a !=0, forecast))
+plt.plot(range(1, 1+len(source)), source)
+plt.plot(range(len(source), len(source)+len(forecast)), forecast)
+plt.grid()
+plt.show()
+