Temporal Data Analysis Algorithm
A Temporal Data Analysis Algorithm is a data analysis algorithm that can be applied by a temporal data analysis system (for temporal data analysis on temporal data).
- AKA: Timeseries Modeling Method.
- Context:
- It can range from being a Retrospective Temporal Data Analysis Algorithm to being a Predictive Temporal Data Analysis Algorithm (such as Data-Driven Temporal Prediction Algorithm).
- It can range from being a Frequency-Domain Temporal Data Analysis Algorithm methods and Time-Domain Temporal Data Analysis Algorithm.
- It can range from being a Univariate Temporal Data Analysis Algorithm (for univariate temporal data) to being a Multivariate Temporal Data Analysis Algorithm.
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- Example(s):
- an ARIMA Algorithm (with an ARIMA model).
- a Graphical Granger Algorithm.
- a Moving Average-based Algorithm.
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- Counter-Example(s):
- See: ARCH Algorithm, Spectrum Analysis, Wavelet Analysis, Auto-Correlation, Cross-Correlation, Scaled Correlation, Autoregressive.
References
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/time_series#Methods_for_analysis Retrieved:2019-10-29.
- Methods for time series analysis may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In the time domain, correlation and analysis can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in the frequency domain.
Additionally, time series analysis techniques may be divided into parametric and non-parametric methods. The parametric approaches assume that the underlying stationary stochastic process has a certain structure which can be described using a small number of parameters (for example, using an autoregressive or moving average model). In these approaches, the task is to estimate the parameters of the model that describes the stochastic process. By contrast, non-parametric approaches explicitly estimate the covariance or the spectrum of the process without assuming that the process has any particular structure.
Methods of time series analysis may also be divided into linear and non-linear, and univariate and multivariate.
- Methods for time series analysis may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In the time domain, correlation and analysis can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in the frequency domain.
2016
- https://github.com/statsmodels/statsmodels/
- Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.
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- Time Series Analysis: models for time series analysis
- Complete StateSpace modeling framework
- Seasonal ARIMA and ARIMAX models.
- VARMA and VARMAX models.
- Dynamic Factor models.
- Markov switching models (MSAR), also known as Hidden Markov Models (HMM)
- Univariate time series analysis: AR, ARIMA.
- Vector autoregressive models, VAR and structural VAR
- Hypothesis tests for time series: unit root, cointegration and others
- Descriptive statistics and process models for time series analysis
- …
- Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/time_series#Methods_for_time_series_analyses Retrieved:2015-10-11.
- Methods for time series analyses may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and recently wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In time domain, correlation analyses can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in frequency domain.
Additionally, time series analysis techniques may be divided into parametric and non-parametric methods. The parametric approaches assume that the underlying stationary stochastic process has a certain structure which can be described using a small number of parameters (for example, using an autoregressive or moving average model). In these approaches, the task is to estimate the parameters of the model that describes the stochastic process. By contrast, non-parametric approaches explicitly estimate the covariance or the spectrum of the process without assuming that the process has any particular structure.
Methods of time series analysis may also be divided into linear and non-linear, and univariate and multivariate.
- Methods for time series analyses may be divided into two classes: frequency-domain methods and time-domain methods. The former include spectral analysis and recently wavelet analysis; the latter include auto-correlation and cross-correlation analysis. In time domain, correlation analyses can be made in a filter-like manner using scaled correlation, thereby mitigating the need to operate in frequency domain.