When reshaping time series data, it is important to ensure that the order of the dimensions is correct. In general, the order of the dimensions will depend on the specific analysis or visualization that you want to perform.
For example, if you are working with a time series dataset that has multiple variables, you might want to reshape the data so that each variable is stored in a separate column. In this case, you would want to ensure that the variable dimension is the last dimension in the reshaped array, since this makes it easier to perform operations on individual variables.
Here is an example of how to reshape a two-dimensional NumPy array of time series data into a three-dimensional array with dimensions (num_vars, num_dates, num_obs_per_var):
import numpy as np# Create a datetime64 index for the time series dataindex = np.arange('2022-01-01', '2022-01-11', dtype='datetime64[D]')# Create a two-dimensional NumPy array to store the datadata = np.array([ [1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9, 9.0, 10.1], [11.2, 12.3, 13.4, 14.5, 15.6, 16.7, 17.8, 18.9, 19.0, 20.1]])# Reshape the data into a three-dimensional array with dimensions (2, 10, 1)reshaped_data = data.reshape((2, 10, 1))# Print the original and reshaped dataprint("Original data:")print(data)print("Reshaped data:")print(reshaped_data) |
In this example, we reshape the data into a three-dimensional array with dimensions (2, 10, 1). The first dimension corresponds to the variables, the second dimension corresponds to the dates, and the third dimension corresponds to the number of observations per variable.
It is important to note that the order of the dimensions can affect the performance of your code. For example, if you are performing operations on individual variables, it is more efficient to have the variable dimension as the last dimension in the array, since this allows you to use NumPy's vectorized operations. On the other hand, if you are performing operations on entire time series, it may be more efficient to have the date dimension as the last dimension, since this allows you to use Dask's chunking and parallelization capabilities.