# The additive model assumes the components of time series are

## Introduction

The additive model is a statistical model that assumes the components of a time series are added together to create the final series. The components can be anything, but they are typically things like trends, seasonality, and noise.

The additive model is a mathematical model which assumes that the components of a time series are linearly added. This model is a good starting point for many time series analysis because it is a simple model to understand and to interpret. The additive model is also known as the linear model.

### Components of the Additive Model

In the additive model, the response of a given treatment is the sum of its individual effects. The main advantage of this approach is that it allows researchers to study the effects of a given treatment while controlling for all other potential confounding variables. This makes it easier to isolate the specific effect of interest.

The additive model has a few key components:
-The response variable is the outcome of interest, such as test scores or percentage of students who graduate.
-The predictor variables are the treatments or other conditions that are being compared.
-The levels are the different values that a predictor variable can take on, such as different doses of a drug or different teaching methods.
-The fixed effects are the specific impacts that each level of a predictor variable has on the response variable. These effects are constant across all values of the other predictor variables.
-The random effects are any factors that could potentially affect the response variable but are not being specifically considered in the study.

One of the advantages of the additive model is that it is relatively easy to understand and interpret. This model can also be extended to multiple variables, which makes it a flexible tool for analyzing time series data.

Another advantage of the additive model is that it can be used to examine the effect of individual variables on the overall time series. This is especially helpful when you have a large number of variables and want to identify which ones have the greatest impact on the time series.

finally, the additive model is less susceptible to outliers than other types of models. This is because the effect of an outlier on the overall time series is usually limited to the period during which the outlier occurs.

The main disadvantage of the additive model is that it does not take into account the interaction of factors. In other words, it assumes that the effect of each factor is independent of the others. This is often not the case in real-world situations.

For example, imagine you are trying to predict how well students will do on a test. You might think that intelligence (a factor) and how much study time they put in (another factor) are both independently related to test scores. However, it is quite possible that study time has a different effect on students with different levels of intelligence. The additive model would not be able to take this into account.

## The Multiplicative Model

The multiplicative model is more appropriate for analyzing time series data with seasonal variation. The multiplicative model is similar to the additive model, except that the seasonal component is multiplied by the trend component instead of being added. In the multiplicative model, the seasonal component and the trend component both affect the level of the time series.

### Components of the Multiplicative Model

There are three components to the multiplicative model: the long-term average, the seasonal component, and the irregular component.

The long-term average is the expected value of the time series in the absence of any seasonality or other factors. It is sometimes called the “level” of the time series.

The seasonal component is a repeating pattern that occurs during specific parts of the year. For example, retail sales are often higher in November and December due to holiday shopping.

The irregular component is everything else that is not captured by the long-term average or seasonal component. This includes one-time events such as a natural disaster, as well as random fluctuations that are not part of any underlying pattern.

### Advantages of the Multiplicative Model

The multiplicative model has several advantages over the additive model. One advantage is that the multiplicative model can accommodate seasonal variation in the data. Another advantage is that the multiplicative model can accommodate a non-normal distribution of the data, which is often the case with time series data. Finally, the multiplicative model can be used to forecast future values of the time series, which is not possible with the additive model.

### Disadvantages of the Multiplicative Model

While the multiplicative model has several advantages, there are also some disadvantages to using this approach. One potential downside is that it can be difficult to interpret the results of the analysis. In addition, the multiplicative model does not account for seasonality, which can be a significant factor in many data sets.

## Conclusion

The additive model is a statistical model which predicts a time series as the sum of several different components. This model is useful for understanding the underlying trends and patterns in a time series data set, and can be used to make forecasts.