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The decomposition of time series is a statistical ... An example of statistical software for this type of decomposition is the program BV4.1 ... Matlab, and Python ...
X-13ARIMA-SEATS, successor to X-12-ARIMA and X-11, is a set of statistical methods for seasonal adjustment and other descriptive analysis of time series data that are implemented in the U.S. Census Bureau's software package. [3]
Traces is a Python library for analysis of unevenly spaced time series in their unaltered form.; CRAN Task View: Time Series Analysis is a list describing many R (programming language) packages dealing with both unevenly (or irregularly) and evenly spaced time series and many related aspects, including uncertainty.
The Berlin procedure (BV) is a mathematical procedure for time series decomposition and seasonal adjustment of monthly and quarterly economic time series. The mathematical foundations of the procedure were developed in 1960's at Technische Universität Berlin and the German Institute for Economic Research (DIW).
Forecasting on time series is usually done using automated statistical software packages and programming languages, such as Julia, Python, R, SAS, SPSS and many others. Forecasting on large scale data can be done with Apache Spark using the Spark-TS library, a third-party package.
The Hodrick–Prescott filter (also known as Hodrick–Prescott decomposition) is a mathematical tool used in macroeconomics, especially in real business cycle theory, to remove the cyclical component of a time series from raw data.
Time-series segmentation is a method of time-series analysis in which an input time-series is divided into a sequence of discrete segments in order to reveal the underlying properties of its source. A typical application of time-series segmentation is in speaker diarization , in which an audio signal is partitioned into several pieces according ...
In the context of forecasting oil futures prices, the multiresolution nature of wavelet packet decomposition enables the forecasting model to capture both high and low-frequency components in the time series, thereby improving the ability to capture the complex patterns and fluctuations inherent in financial data. [15]