Search results
Results from the WOW.Com Content Network
The Journal of Time Series Analysis is a bimonthly peer-reviewed academic journal covering mathematical statistics as it relates to the analysis of time series data. It was established in 1980 and is published by John Wiley & Sons. The editor-in-chief is Robert Taylor (University of Essex).
Bayesian structural time series (BSTS) model is a statistical technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other applications. The model is designed to work with time series data. The model has also promising application in the field of analytical marketing. In particular, it can be used ...
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.
This is an important technique for all types of time series analysis, especially for seasonal adjustment. [2] It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behavior.
Ideally, unevenly spaced time series are analyzed in their unaltered form. However, most of the basic theory for time series analysis was developed at a time when limitations in computing resources favored an analysis of equally spaced data, since in this case efficient linear algebra routines can be used and many problems have an explicit ...
A time series database is a software system that is optimized for storing and serving time series through associated pairs of time(s) and value(s). [1] In some fields, time series may be called profiles, curves, traces or trends. [ 2 ]
In stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analysing time series that appear to be long-memory processes (diverging correlation time , e.g. power-law decaying autocorrelation function ) or 1/f noise .
[1] [2] Given two completely unrelated but integrated (non-stationary) time series, the regression analysis of one on the other will tend to produce an apparently statistically significant relationship and thus a researcher might falsely believe to have found evidence of a true relationship between these variables.