If you followed along with the Block bootstrapping time series data recipe, you are now aware of a simple bootstrapping scheme for time series data. The moving block bootstrapping algorithm is a bit more complicated. In this scheme, we generate overlapping blocks by moving a fixed size window, similar to the moving average. We then assemble the blocks to create new data samples.
In this recipe, we will apply the moving block bootstrap to annual temperature data to generate lists of second difference medians and the slope of an AR(1) model. This is an autoregressive model with lag 1. Also, we will try to neutralize outliers and noise with a median filter.
The following code snippets are from the moving_boot.ipynb file in this book's code bundle:
The imports are as follows:
import dautil as dl import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import ch6util from scipy.signal import medfilt from...