Discover the intricacies of time series and sequential data analysis with the Analyzing Time Series and Sequential Data course offered by SAS. Through hands-on training, you will gain expertise in exploring time sequences, creating features, and selecting the most suitable ones. Additionally, you will learn to build and manage large-scale forecasting systems using SAS Visual Forecasting tools.
The course content includes:
Upon completion, you will be equipped with advanced skills in time series and sequential data analysis, making you proficient in leveraging various models to identify, estimate, and forecast signal components of interest. This course is tailored for analysts with a quantitative background or domain experts seeking to enhance their time-series toolbox.
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This course comprises three modules covering data exploration and feature creation, building large-scale forecasting systems, and modeling time series and sequential data using traditional, Bayesian, and machine learning approaches.
This module focuses on data exploration, feature creation, and selection for time sequences. It covers topics such as binning, smoothing, transformations, spectral analysis, singular spectrum analysis, distance measures, and motif analysis. Analysts with a quantitative background or domain experts can enhance their time-series toolbox by gaining expertise in these areas.
In this module, you will learn to develop and maintain large-scale forecasting projects using SAS Visual Forecasting tools. Emphasis is placed on selecting appropriate methods for data creation, variable transformations, model generation, and model selection. This course is suitable for analysts interested in augmenting their machine learning skills with analysis tools for handling and managing time series data.
This module covers building, refining, and interpreting models designed for sequential series. It includes traditional Box-Jenkins approach, Bayesian modeling, and machine learning algorithms for time series. Analysts will learn to improve forecasting precision by combining the strengths of different modeling approaches. The course utilizes a variety of software tools and assumes some prior knowledge of Bayesian analysis and machine learning models.
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