Differencing: meaning, definitions and examples

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differencing

 

[ ˈdɪf.ər.ən.sɪŋ ]

Noun
Context #1 | Noun

mathematics, statistics

Differencing is a statistical method used to transform a time series dataset to make it stationary, often employed in time series analysis. This technique involves subtracting the previous observation from the current observation, which can help remove trends and seasonality from the data. It is particularly important in autoregressive integrated moving average (ARIMA) models, which require stationary data to provide accurate forecasting. By applying differencing, analysts can better understand underlying patterns in data and make more informed predictions.

Synonyms

deviation, differentiation, subtraction.

Examples of usage

  • The analyst applied differencing to the dataset.
  • Differencing is crucial for time series forecasting.
  • She used differencing to stabilize the time series.
  • After differencing, the data showed no trend.

Translations

Translations of the word "differencing" in other languages:

🇵🇹 diferenciação

🇮🇳 भेद करना

🇩🇪 Differenzierung

🇮🇩 perbedaan

🇺🇦 різниця

🇵🇱 różnicowanie

🇯🇵 差分

🇫🇷 différenciation

🇪🇸 diferenciación

🇹🇷 farklandırma

🇰🇷 차별화

🇸🇦 تمييز

🇨🇿 odlišení

🇸🇰 odlíšenie

🇨🇳 差异化

🇸🇮 diferenciranje

🇮🇸 mismunur

🇰🇿 айырмашылық

🇬🇪 განსხვავება

🇦🇿 fərqləndirmə

🇲🇽 diferenciación

Etymology

The term 'differencing' has its roots in the mathematical concept of 'difference', which pertains to the result of subtracting one quantity from another. The practice of differencing in analysis can be traced back to traditional statistics and calculus in the 19th century. As time series analysis emerged as an important field within statistics, differencing became a standard method to achieve stationarity, which is crucial for modeling temporal data effectively. Over time, with the development of more sophisticated forecasting techniques and software, differencing has solidified its place as a fundamental tool in the toolkit of statisticians and data scientists alike.