python - 如何将残差转换为原始值 Python statsmodels

Question

我可以使用这种方式分解时间序列数据 -

from statsmodels.tsa.seasonal import seasonal_decompose
decomposition = seasonal_decompose(ts)

trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid

我的问题是如何将这个残值转换为原始值。这样我就可以用它们来预测 future 的值(value)。

Answer 1

我尝试添加原始趋势和季节性，但效果不佳。因此，当我将预测结果与原始结果进行比较时，我只是使用带有残差的预测结果。

efrom statsmodels.tsa.seasonal import seasonal_decompose

# trend, seasonality are separated out from data, and we can model the residuals
decomposition = seasonal_decompose(ts_log)
trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid

# AR model
model = ARIMA(ts_log, order=(2, 1, 0))  
results_AR = model.fit(disp=-1) 
plt.figure(figsize=(20,10))
plt.plot(ts_log_decompose)
plt.plot(results_AR.fittedvalues, color='red')
result = (results_AR.fittedvalues-ts_log_decompose)**2
result.dropna(inplace=True)
plt.title('Decompose RSS: %.4f'% sum(result))
plt.show()

我尝试了 AR、MA、ARIMA 模型，发现 AR 模型的 RSS 最低。所以现在我正在用AR模型进行预测。

predictions_AR = pd.Series(results_AR.fittedvalues, copy=True)
print predictions_AR.head()
plt.figure(figsize=(20,10))
plt.plot(series, color='red')
plt.plot(predictions_ARIMA, color='green')
result = (predictions_AR-residual)**2
result.dropna(inplace=True)
plt.title('RMSE: %.4f'% np.sqrt(sum(result)/len(series)))
plt.show()

效果很好:

如果你想检查我的所有代码:https://github.com/hanhanwu/Hanhan_Data_Science_Practice/blob/master/sequencial_analysis/try_LSTM.ipynb

只需向下滚动即可分解方法