autocorrelation

来源： https://stackoverflow.com/questions/64339451/why-in-fable-forecast-libraries-residual-diagnostics-of-arima-models-is-done-wit

问题 I have two different excel files. One of them is including time series data (268943 accident time rows) as below Datetime 0 2010-01-01 14:00:00 1 2010-01-01 13:00:00 2 2010-01-01 21:00:00 3 2010-01-01 13:00:00 4 2010-01-01 21:00:00 ... ... 268938 2018-08-06 11:25:00 268939 2018-08-06 10:30:00 268940 2018-08-06 10:00:00 268941 2018-08-06 11:37:00 268942 2018-08-06 09:00:00 [268943 rows x 1 columns] dtype = datetime64[ns] The other file is blood sugar level of 14 workers measured daily from 8

问题 I am using python 3.7. I am performing time series forecasting using an ARIMA model. I am assessing the properties of my data for ARIMA using an Autocorrelation Plot - specifically using autocorrelation_plot from pandas.plotting. My data has 50,000 records or so, making the plot extremely busy and hard to pick out any specific trends. Is there a way to limit the x-axis to bring the first few hundred lags more into focus? I can't share the actual plot, but my code is as follow: import pandas

问题 I followed the advice of defining the autocorrelation function in another post: def autocorr(x): result = np.correlate(x, x, mode = 'full') maxcorr = np.argmax(result) #print 'maximum = ', result[maxcorr] result = result / result[maxcorr] # <=== normalization return result[result.size/2:] however the maximum value was not "1.0". therefore I introduced the line tagged with "<=== normalization" I tried the function with the dataset of "Time series analysis" (Box - Jenkins) chapter 2. I expected