Analiza danych IoT w Pythonie
Matthias Voppichler
IT Developer
series[t] = trend[t] + seasonal[t] + residual[t]
20.2 = 14.9 + 4.39 + 0.91
import statsmodels.api as sm # Run seasonal decompose decomp = sm.tsa.seasonal_decompose(data["temperature"]) print(decomp.seasonal.head()) decomp.plot()
import statsmodels.api as sm # Run seasonal decompose decomp = sm.tsa.seasonal_decompose(data["temperature"]) print(decomp.seasonal.head())
decomp.plot()
timestamp 2018-10-01 00:00:00 -3.670394 2018-10-01 01:00:00 -3.987451 2018-10-01 02:00:00 -4.372217 2018-10-01 03:00:00 -4.534066 2018-10-01 04:00:00 -4.802165 Freq: H, Name: temperature, dtype: float64
# Plot the timeseries plt.plot(data["temperature"], label="temperature") decomp = sm.tsa.seasonal_decompose(data["temperature"]) # Plot trend and seasonality plt.plot(decomp.trend, label="trend") plt.plot(decomp.seasonal, label="seasonal") plt.show()
# Plot the timeseries plt.plot(data["temperature"], label="temperature")
decomp = sm.tsa.seasonal_decompose(data["temperature"]) # Plot trend and seasonality plt.plot(decomp.trend, label="trend") plt.plot(decomp.seasonal, label="seasonal") plt.show()
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