autocorr.py

import utils


def tsplot(y, lags=None, figsize=(10, 8), style='bmh'):
    import pandas as pd
    import matplotlib.pyplot as plt
    import statsmodels.tsa.api as smt
    import statsmodels.api as sm
    import scipy.stats as scs

    if not isinstance(y, pd.Series):
        y = pd.Series(y)
    with plt.style.context(style):
        fig = plt.figure(figsize=figsize)
        # mpl.rcParams['font.family'] = 'Ubuntu Mono'
        layout = (3, 2)
        ts_ax = plt.subplot2grid(layout, (0, 0), colspan=2)
        acf_ax = plt.subplot2grid(layout, (1, 0))
        pacf_ax = plt.subplot2grid(layout, (1, 1))
        qq_ax = plt.subplot2grid(layout, (2, 0))
        pp_ax = plt.subplot2grid(layout, (2, 1))

        y.plot(ax=ts_ax)
        ts_ax.set_title('Time Series Analysis Plots')
        # Autocorrelation
        smt.graphics.plot_acf(y, lags=lags, ax=acf_ax, alpha=0.5)
        # Partial Autocorrelation
        smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax, alpha=0.5)
        # QQ Plot
        sm.qqplot(y, line='s', ax=qq_ax)
        qq_ax.set_title('QQ Plot')
        # Probability plot
        scs.probplot(y, sparams=(y.mean(), y.std()), plot=pp_ax)

        plt.tight_layout()
    return plt


fname = "data/gold_daily.pkl"
df_gold_daily = utils.load_pickle(fname)
# utils.standard_plot(df_gold_daily, column_name='Gold')
days_range = 700
gold_daily_series = df_gold_daily['Gold'][-days_range:]

plt = tsplot(gold_daily_series, lags=50)
plt.show()

if __name__ == "__main__":
    pass