respond to your colleagues in one or more of the following ways:

• Ask a probing question, substantiated with additional background information, evidence, or research.
• Share an insight from having read your colleagues' postings, synthesizing the information to provide new perspectives.
• Offer and support an alternative perspective, using readings from the classroom or from your own research in the Walden Library.
• Validate an idea with your own experience and additional research.
• Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.
• Expand on your colleagues' postings by providing additional insights or contrasting perspectives based on readings and evidence.

Colleagues post

There are many types of forecasting methods, and these methods can be divided into three groups. The three groups are: judgmental methods, extrapolation (or time series) methods and econometric (or casual) methods (Albright et al., 2017). The judgmental methods are nonquantitative models. The extrapolation models are quantitative models that use previous data of a time series variable to forecast future values of the variable (Albright et al., 2017). Types of this model include: trend-based regression, autoregression, moving averages, and exponential smoothing (Albright et al., 2017). The econometric models use regression to forecast a time series variable by using other explanatory time series variables (Albright et al., 2017).

A time series model is a set of data points ordered in time to forecast the future (Peixeiro, 2023). There are three main characteristics of time series: autocorrelation, seasonality, and stationarity. Autocorrelation is the similarity between observations as a function of the lag time between (Peixeiro, 2023). Seasonality refers to periodic fluctuations (Peixeiro, 2023). Stationarity in time series means it has a constant mean and variance, and its covariance is independent of time (Peixeiro, 2023).

Time series analysis can be used for many, many things in health care. It can show genetic and lifestyle risks, time and stage of a diagnosis, weekly admissions versus discharge, annual expenditures on healthcare, etc. Different models of time series analysis can be used to make predictions. The models include moving average, exponential smoothing, double exponential smoothing, triple exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) (Peixeiro, 2023).

References

Albright, S. C., & Winston, W. L. (2017).Business analytics: Data analysis and decision making(6th ed.). Stamford, CT: Cengage Learning.

• Chapter 12, "Time Series Analysis and Forecasting"

Peixeiro, M. (2023, August 1). The Complete Guide to Time Series models. Built In. https://builtin.com/data-science/time-series-model