Graduate Certificate in Modeling Techniques for Time Series
-- ViewingNowThe Graduate Certificate in Modeling Techniques for Time Series is a crucial course designed to equip learners with essential skills in time series analysis. This program is significant in today's data-driven world, where businesses rely heavily on accurate forecasting and trend analysis.
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⢠Time Series Analysis: An overview of time series analysis, including key concepts, components, and methods.
⢠ARIMA Modeling: Detailed study of Autoregressive Integrated Moving Average (ARIMA) modeling techniques, including identification, estimation, and diagnostic checking.
⢠Exponential Smoothing: Study of exponential smoothing methods, including simple, Holt, and Holt-Winters exponential smoothing.
⢠State Space Models: Introduction to state space models and their applications in time series analysis.
⢠Seasonal Analysis: Analysis of seasonal time series data, including decomposition, regression, and ARIMA modeling.
⢠Long Memory Processes: Study of long memory processes, including fractional integration and fractional differencing.
⢠Multivariate Time Series: Analysis of multivariate time series data, including vector autoregression and dynamic factor models.
⢠Nonlinear Time Series: Introduction to nonlinear time series models, including threshold models, Markov switching models, and nonlinear state space models.
⢠Time Series Forecasting: Application of time series modeling techniques to forecasting, including model selection, evaluation, and combination.
⢠Case Studies: Application of time series modeling techniques to real-world data, including data preprocessing, model selection, and interpretation of results.
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