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110 lines
2.9 KiB
Python
110 lines
2.9 KiB
Python
import matplotlib.pyplot as plt
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import pandas as pd
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from statsmodels.tsa.holtwinters import ExponentialSmoothing
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import requests
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import json
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# ts = pd.read_csv("d:\\files\\Features count.csv")
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# ts.plot()
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# plt.grid()
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# plt.show()
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# moving_avg = ts.rolling(4).mean()
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# ts.plot()
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# plt.plot(moving_avg, color='green', label='Moving average')
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# plt.legend()
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# plt.grid()
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# model = ExponentialSmoothing(ts, trend="additive", seasonal="additive", seasonal_periods=5)
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# fit1 = model.fit()
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# pred1 = fit1.forecast(5)
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# plt.plot(ts, label='Features count')
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# plt.plot(pred1, color='red', label='Holt-Winters forecast')
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# plt.legend()
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# plt.grid()
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ts = pd.read_csv("d:\\files\\Testing time.csv")
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model = ExponentialSmoothing(ts, trend="additive", seasonal="additive", seasonal_periods=7)
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fit1 = model.fit()
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pred1 = fit1.forecast(10)
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plt.plot(ts, label='Testing time')
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plt.plot(pred1, color='red', label='Holt-Winters forecast')
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plt.legend()
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plt.grid()
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plt.show()
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# Использовать внешний сервис прогнозирования
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time_series = {
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"originalTimeSeries": {
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"values": [
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{
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"date": "2023-04-16T06:29:50.668476",
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"value": 1
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},
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{
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"date": "2023-04-17T06:29:50.668476",
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"value": 2
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},
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{
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"date": "2023-04-18T06:29:50.668476",
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"value": 3
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},
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{
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"date": "2023-04-19T06:29:50.668476",
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"value": 4
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},
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{
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"date": "2023-04-20T06:29:50.668476",
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"value": 5
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},
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{
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"date": "2023-04-21T06:29:50.668476",
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"value": 6
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},
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{
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"date": "2023-04-22T06:29:50.668476",
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"value": 5
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},
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{
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"date": "2023-04-23T06:29:50.668476",
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"value": 4
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},
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{
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"date": "2023-04-24T06:29:50.668476",
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"value": 3
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},
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{
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"date": "2023-04-25T06:29:50.668476",
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"value": 2
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},
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{
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"date": "2023-04-26T06:29:50.668476",
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"value": 1
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},
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{
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"date": "2023-04-27T06:29:50.668476",
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"value": 2
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}
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],
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"name": 'test'
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},
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"countForecast": 5
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}
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source = []
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for v in time_series['originalTimeSeries']['values']:
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source.append(v['value'])
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url = "http://time-series.athene.tech/api/1.0/getForecast"
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headers = {'Content-type': 'application/json'}
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r = requests.post(url, data=json.dumps(time_series), headers=headers)
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forecast = [item['value'] for item in r.json()['timeSeries']['values']]
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plt.plot(range(1, 1+len(source)), source)
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plt.plot(range(len(source), len(source)+len(forecast)), forecast)
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plt.grid()
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plt.show()
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