predict.py 文件源码

def predict(df,steps):
    start = df.index[0].year
    end = df.index[-1].year
    years_captured = [idx.year for idx in df.index]
    years_inclusive = [elem for elem in range(start, end+1)]
    s = df.T.squeeze()
    for year in years_inclusive:
        if not year in years_captured:
            s = s.set_value(datetime.datetime(year=year,month=1,day=1),math.nan)
    s.sort_index(inplace=True)
    s = s.interpolate()
    data = s.to_frame()
    model_order = brute_search(data)
    model_order = tuple([int(elem) for elem in model_order])
    model = sm.tsa.ARIMA(data, model_order).fit(disp=0)
    return model.forecast(steps=steps)[0], end