A Course in Time Series Analysis

By S. Rao. 2001

265 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7a7efaf3f1e6eaad8acf3dca17ef98b79d64ef62

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In Artificial Intelligence

Citations in the corpus

Time series interpolation via global optimization of moments fitting (2013, background): “This series, analyzed by [31], represents 176 quarterly US real GNP (in dollars) from the first quarter of 1947 to the first quarter of 1991.”
Model identification of ARIMA family using genetic algorithms (2005, methodology): “The data set used in [48] is composed of seasonally adjusted quarterly US unemployment rates from 1948 to 1993.”
A Review of Some Modern Approaches to the Problem of Trend Extraction (2012, background): “We also mention the RegComponent software (Bell, 2004), which uses a Structural Approach with ARIMA models (although extensions now allow for a Decomposition Approach as well); this was one of the first programs to simultaneously handle smoothing by SSF methods and also estimate fixed regression eff”
Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model (2017, background): “The trend and periodical components lead to nonstationary time series that is removed with non-seasonal and seasonal differencing, respectively (Salas et al. 1988; Cryer and Chan 2008; Peña et al. 2011).”
An Economic Evaluation of Empirical Exchange Rate Models (2009, methodology): “Furthermore, by fixing δ rather than λ, we are implicitly interpreting quadratic utility as an approximation to a nonquadratic utility function, with the approximating choice of λ dependent on wealth. The estimate of expected quadratic utility given in Equation (14) is used to implement the Fleming,”
Performance of state space and ARIMA models for consumer retail sales forecasting (2015, methodology): “To evaluate the out-ofsample forecast accuracy using an in-sample set of size m n < (where n is the total number of observations), the most commonly used scale-dependent statistics are the mean error (ME), the mean absolute error (MAE) and the root mean squared error (RMSE) defined as follows [17]:”
Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities (2013, background): “Excellent references on univariate and multivariate time series models are Peña et al. (2001), Tsay (2005) and Shumway and Stoffer (2006).”