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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.
Python has the statsmodelsS package which includes many models and functions for time series analysis, including ARMA. Formerly part of the scikit-learn library, it is now stand-alone and integrates well with Pandas. PyFlux has a Python-based implementation of ARIMAX models, including Bayesian ARIMAX models.
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.
In time series analysis, singular spectrum analysis (SSA) is a nonparametric spectral estimation method. It combines elements of classical time series analysis, multivariate statistics , multivariate geometry, dynamical systems and signal processing .
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.
ROOT Analysis Framework 6.24.00 (15 April 2021) Yes GNU GPL: GUI: C++ C++, Python SageMath >100 developers worldwide 9.5 (30 January 2022; 2 years ago (10] Yes GNU GPL: CLI & GUI: Python, Cython Python Salstat: Alan J. Salmoni, Mark Livingstone 16 May 2014 () Yes GNU GPL: CLI & GUI: Python, NumPy, SciPy: Python SAS: SAS Institute
In time series analysis used in statistics and econometrics, autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models are generalizations of the autoregressive moving average (ARMA) model to non-stationary series and periodic variation, respectively.
Volatility and Time Series Econometrics: Essays in Honor of Robert Engle (1st ed.). Oxford: Oxford University Press. pp. 137–163. ISBN 9780199549498. Enders, W. (2004). "Modelling Volatility". Applied Econometrics Time Series (Second ed.). John-Wiley & Sons. pp. 108–155. ISBN 978-0-471-45173-0. Engle, Robert F. (1982). "Autoregressive ...