Time Analysis Using SAS Enterprise Guide: SpringerBriefs in Statistics

Time series analysis is a statistical method that is used to analyze data that is collected over time. This type of data is often used in a variety of fields, such as economics, finance, and healthcare. Time series analysis can be used to identify trends and patterns in the data, and it can also be used to make predictions about future values.

SAS Enterprise Guide is a statistical software package that is used for a variety of data analysis tasks. It includes a number of features that make it well-suited for time series analysis, such as a graphical user interface that makes it easy to visualize data and a variety of statistical procedures that can be used to analyze time series data.

This book provides a comprehensive guide to time series analysis using SAS Enterprise Guide. It covers a wide range of topics, from basic concepts to advanced techniques. The book is written for practitioners and researchers who need to analyze time series data, and it provides a valuable resource for anyone who wants to learn more about this important statistical method.

Time Series Analysis Using SAS Enterprise Guide (SpringerBriefs in Statistics)

by Fred Herbert

4 out of 5

Language	:	English
File size	:	10796 KB
Screen Reader	:	Supported
Print length	:	139 pages

FREE

A time series is a sequence of data points that are collected over time. The data points can be measured at regular intervals, such as daily, weekly, or monthly, or they can be measured at irregular intervals. Time series data is often used to track the progress of a system or process over time.

There are a number of important concepts that are related to time series analysis. These concepts include:

• Trend: A trend is a long-term increase or decrease in the data. Trends can be linear, exponential, or polynomial.
• Seasonality: Seasonality is a pattern in the data that repeats over time. Seasonality can be caused by a number of factors, such as the time of year or the day of the week.
• Cyclicity: Cyclicity is a pattern in the data that repeats over a period of time that is longer than a year. Cyclicity can be caused by a number of factors, such as the business cycle or the sunspot cycle.
• Irregularity: Irregularity is a random variation in the data that cannot be explained by any of the other concepts.

There are a number of different time series analysis techniques that can be used to identify trends, patterns, and anomalies in the data. These techniques include:

• Smoothing: Smoothing is a technique that is used to remove noise from the data. Smoothing can be done using a variety of methods, such as moving averages, exponential smoothing, and loess.
• Decomposition: Decomposition is a technique that is used to break down the time series into its component parts. Decomposition can be done using a variety of methods, such as seasonal decomposition of time series (STL) and moving averages.
• Forecasting: Forecasting is a technique that is used to predict future values of the time series. Forecasting can be done using a variety of methods, such as autoregressive integrated moving average (ARIMA) models and exponential smoothing models.

SAS Enterprise Guide is a statistical software package that includes a number of features that make it well-suited for time series analysis. These features include:

• A graphical user interface that makes it easy to visualize data: SAS Enterprise Guide includes a number of graphical tools that make it easy to visualize time series data. These tools include line charts, bar charts, and scatter plots.
• A variety of statistical procedures that can be used to analyze time series data: SAS Enterprise Guide includes a number of statistical procedures that can be used to analyze time series data. These procedures include procedures for smoothing, decomposition, forecasting, and other time series analysis tasks.
• A macro language that can be used to automate tasks: SAS Enterprise Guide includes a macro language that can be used to automate tasks. This macro language can be used to create custom time series analysis procedures, and it can also be used to integrate SAS Enterprise Guide with other software programs.

Time series analysis is a powerful statistical method that can be used to identify trends, patterns, and anomalies in data that is collected over time. SAS Enterprise Guide is a statistical software package that is well-suited for time series analysis, and it provides a valuable resource for anyone who wants to learn more about this important statistical method.

Time Series Analysis Using SAS Enterprise Guide (SpringerBriefs in Statistics)

by Fred Herbert

4 out of 5

Language	:	English
File size	:	10796 KB
Screen Reader	:	Supported
Print length	:	139 pages

FREE