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The time series included yearly, quarterly, monthly, daily, and other time series. In order to ensure that enough data was available to develop an accurate forecasting model, minimum thresholds were set for the number of observations: 14 for yearly series, 16 for quarterly series, 48 for monthly series, and 60 for other series. [1]
Time series forecasting is the use of a model to predict future values based on previously observed values. Generally, time series data is modelled as a stochastic ...
This forecasting method is only suitable for time series data. [17] Using the naïve approach, forecasts are produced that are equal to the last observed value. This method works quite well for economic and financial time series, which often have patterns that are difficult to reliably and accurately predict. [17]
Markov-chains have been used as a forecasting methods for several topics, for example price trends, [8] wind power [9] and solar irradiance. [10] The Markov-chain forecasting models utilize a variety of different settings, from discretizing the time-series [ 9 ] to hidden Markov-models combined with wavelets [ 8 ] and the Markov-chain mixture ...
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 ...
Tay, Francis EH, and Lijuan Cao. "Application of support vector machines in financial time series forecasting." Omega 29.4 (2001): 309–317. Xiong, Tao, Yukun Bao, and Zhongyi Hu. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices." Energy Economics 40 (2013): 405–415.
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.
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 ...