Data Science, Timeseries, Time Series Analysis, Arima Model, Forecasting.
np.arange(n) data = 50 + 10 * np.sin(2 * np.pi * t / 7) + np.random.normal(0, 5, n) # Decompose the time series using wavelet transform # Break down the data into different frequency components using wavelet transform coeffs = pywt.wavedec(data, 'db4', level=3) # Reconstruct the smoothed version of