Oppiskele Expanding Window Features

Pyyhkäise näyttääksesi valikon

Expanding window statistics are a powerful tool for extracting cumulative patterns from time series data. Unlike rolling windows, which calculate metrics over a fixed number of recent observations, expanding windows compute statistics from the start of the series up to the current point. This approach is especially useful when you want to capture long-term trends or overall progress, such as cumulative sums, means, or other aggregations that reflect all available information up to each time step. Expanding window features are commonly used in financial analysis to monitor running totals or averages, in process monitoring to track ongoing performance, and in any scenario where the cumulative effect is more meaningful than short-term fluctuations.


              1234567891011121314151617
            
import pandas as pd

# Create a sample time series
data = {'value': [10, 12, 13, 15, 18, 20]}
index = pd.date_range('2023-01-01', periods=6, freq='D')
series = pd.Series(data['value'], index=index)

# Calculate expanding mean and expanding sum
expanding_mean = series.expanding().mean()
expanding_sum = series.expanding().sum()

print("Original Series:")
print(series)
print("\nExpanding Mean:")
print(expanding_mean)
print("\nExpanding Sum:")
print(expanding_sum)

Oliko kaikki selvää?

Kiitos palautteestasi!

Osio 1. Luku 5

Kysy tekoälyä

Kysy mitä tahansa tai kokeile jotakin ehdotetuista kysymyksistä aloittaaksesi keskustelumme

Osio 1. Luku 5