两个不同分辨率的时间序列之间的最大差异

Question

I have two time series data that gives the electricity demand in one-hour resolution and five-minute resolution. I am trying to find the maximum difference between these two time series. So the one-hour resolution data has 8760 rows (hourly for an year) and the 5-minute resolution data has 104,722 rows (5-minutly for an year).

I can only think of a method that will expand the hourly data into 5 minute resolution that will have 12 times repeating of the hourly data and find the maximum of the difference of the two data sets.

If this technique is the way to go, is there an easy way to convert my hourly data into 5-minute resolution by repeating the hourly data 12 times?

for your reference I posted a plot of this data for one day.

PS> I am using Python to do this task

Answer 1

Numpy's .repeat() function

You can change your hourly data into 5-minute data by using numpy's repeat function

import numpy as np

np.repeat(hourly_data, 12)

Answer 2

I would strongly recommend against converting the hourly data into five-minute data. If the data in both cases refers to the mean load of those time ranges, you'll be looking at more accurate data if you group the five-minute intervals into hourly datasets. You'd get more granularity the way you're talking about, but the granularity is not based on accurate data, so you're not actually getting more value from it. If you aggregate the five-minute chunks into hourly chunks and compare the series that way, you can be more confident in the trustworthiness of your results.

In order to group them together to get that result, you can define a function like the following and use the apply method like so:

def to_hour(date):
    date = date.strftime("%Y-%m-%d %H:00:00")
    date = dt.strptime(date, "%Y-%m-%d %H:%M:%S")
    return date

df['Aggregated_Datetime'] = df['Original_Datetime'].apply(lambda x: to_hour(x))
df.groupby('Aggregated_Datetime').agg('Real-Time Lo