In this course you learn to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data, or ARMA, to models for trend and seasonality, ARIMA, and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next. The basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects. Machine learning algorithms for time series is the third approach. Gradient boosting and recurrent neural network algorithms are particularly well suited for accommodating nonlinear relationships in the data. Examples are provided to build intuition on the effective use of these algorithms.
Modeling Time Series and Sequential Data
Ce cours fait partie de Spécialisation Analyzing Time Series and Sequential Data
Instructeurs : Chip Wells
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Il y a 8 modules dans ce cours
In this module you get an overview of the courses in this specialization and what you can expect.
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1 vidéo1 lecture
In this module, you get an idea of the scope of this course and learn to use the SAS Virtual Lab to do the practices in the course.
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1 vidéo2 lectures1 élément d'application
This module reviews fundamental time series ideas. You learn about the basic components of systematic variation in time series data and some simple model specifications, such as the autoregressive order one and the random walk. You also learn about Exponential smoothing models or ESMs, selecting a champion ESM, and generating forecasts on time series.
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11 vidéos2 devoirs
This module has four parts. The first part describes traditional models for stationary data: Auto Regressive Moving Average or ARMA models. The second part describes how the ARMA framework is generalized to accommodate trend variation. This involves integration, and results in the ARIMA model. The third part describes how the ARIMA model is adapted to handle seasonal variation in the data. The fourth and final part of the module introduces the dynamic regression or ARIMAX model and describes concepts related to identifying transfer function components and specifying ARIMAX models.
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26 vidéos2 devoirs1 élément d'application
In this module, we combine the worlds of time series and Bayesian analysis. We begin with a brief review of Bayesian analysis. We then explore how to incorporate autoregressive, seasonal, and exogenous components in a Bayesian time series. We conclude with a discussion on Bayesian scoring and posterior predictive distributions.
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10 vidéos8 devoirs1 élément d'application
In this module you learn how to use SAS machine learning tools to forecast individual time series. You learn to prepare the time series data for use with the machine learning tools, and how to build and score forecasting models using these tools. We focus on gradient boosting and recurrent neural network models and discuss when it would be useful to use these methods.
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8 vidéos1 lecture5 devoirs1 élément d'application
This module describes how forecasts that are generated externally to the forecasting system can be accommodated in SAS Visual Forecasting. We'll use external forecasts to create a combined or ensemble forecast that has the potential to improve forecast precision relative to the constituent, external forecasts. This module concludes with a discussion of hybrid model forecasts that combine traditional and machine learning approaches to forecasting.
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9 vidéos1 devoir1 élément d'application
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