Search results
Results from the WOW.Com Content Network
Ménière's disease (MD) is a disease of the inner ear that is characterized by potentially severe and incapacitating episodes of vertigo, tinnitus, hearing loss, and a feeling of fullness in the ear. [3] [4] Typically, only one ear is affected initially, but over time, both ears may become involved. [3]
Dizziness affects approximately 20–40% of people at some point in time, while about 7.5–10% have vertigo. [3] About 5% have vertigo in a given year. [10] It becomes more common with age and affects women two to three times more often than men. [10] Vertigo accounts for about 2–3% of emergency department visits in the developed world. [10]
Prosper Menière (18 June 1799 – 7 February 1862) was a French medical doctor who first identified that the inner ear could be the source of a condition combining vertigo, hearing loss and tinnitus, [1] which is now known as Ménière's disease.
In time series analysis, the Box–Jenkins method, [1] named after the statisticians George Box and Gwilym Jenkins, applies autoregressive moving average (ARMA) or autoregressive integrated moving average (ARIMA) models to find the best fit of a time-series model to past values of a time series.
Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
A time series measures the progression of one or more quantities over time. For instance, the figure above shows the level of water in the Nile river between 1870 and 1970. Change point detection is concerned with identifying whether, and if so when, the behavior of the series changes significantly. In the Nile river example, the volume of ...
For example, time series are usually decomposed into: , the trend component at time t, which reflects the long-term progression of the series (secular variation). A trend exists when there is a persistent increasing or decreasing direction in the data. The trend component does not have to be linear. [1]
In time series analysis, the moving-average model (MA model), also known as moving-average process, is a common approach for modeling univariate time series. [1] [2] The moving-average model specifies that the output variable is cross-correlated with a non-identical to itself random-variable.