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  2. Bayesian structural time series - Wikipedia

    en.wikipedia.org/.../Bayesian_structural_time_series

    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 ...

  3. Box–Jenkins method - Wikipedia

    en.wikipedia.org/wiki/Box–Jenkins_method

    The original model uses an iterative three-stage modeling approach: Model identification and model selection: making sure that the variables are stationary, identifying seasonality in the dependent series (seasonally differencing it if necessary), and using plots of the autocorrelation (ACF) and partial autocorrelation (PACF) functions of the dependent time series to decide which (if any ...

  4. Time series - Wikipedia

    en.wikipedia.org/wiki/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.

  5. Bayesian hierarchical modeling - Wikipedia

    en.wikipedia.org/wiki/Bayesian_hierarchical_modeling

    Data structure for the hierarchical modeling retains nested data structure. The hierarchical form of analysis and organization helps in the understanding of multiparameter problems and also plays an important role in developing computational strategies.

  6. Autoregressive moving-average model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_moving...

    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.

  7. Group method of data handling - Wikipedia

    en.wikipedia.org/wiki/Group_method_of_data_handling

    It became clear that full physical models cannot be used for long-term forecasting. It was proved, that non-physical models of GMDH are more accurate for approximation and forecast than physical models of regression analysis. Two-level algorithms which use two different time scales for modeling were developed.

  8. Multivariate adaptive regression spline - Wikipedia

    en.wikipedia.org/wiki/Multivariate_adaptive...

    These models and extensions to include moving average spline models are described in "Univariate Time Series Modelling and Forecasting using TSMARS: A study of threshold time series autoregressive, seasonal and moving average models using TSMARS". Bayesian MARS (BMARS) uses the same model form, but builds the model using a Bayesian approach. It ...

  9. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    All data files contain anomalies, unless otherwise noted. None 50+ files CSV Anomaly detection: 2016 (continually updated) [328] Numenta Skoltech Anomaly Benchmark (SKAB) Each file represents a single experiment and contains a single anomaly. The dataset represents a multivariate time series collected from the sensors installed on the testbed.