Time Series Analysis

python
Run Inline
import pandas as pd
import numpy as np
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.seasonal import seasonal_decompose
# Generate time series
dates = pd.date_range('2020-01-01', periods=365, freq='D')
ts = pd.Series(
    np.random.randn(365).cumsum() + 10,
    index=dates
)
# Decomposition
decomp = seasonal_decompose(ts, model='additive', period=7)
decomp.plot()
# ARIMA model
model = ARIMA(ts, order=(1, 1, 1))
results = model.fit()
print(results.summary())
# Forecast
forecast = results.forecast(steps=10)
print(forecast)