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Time series regression model

Time series regression model

150 words reply agree or disagree to each questions


The four major elements of the time series regression model consist of the trend component, seasonal component, cyclic component, and noise component. The trend component represents the pattern the observations take. Common trends are linear, exponential, and s-shaped. The seasonal component represents a repeating pattern over time, and is not in every time series. The cyclic component is similar to the seasonal component, and uses historical data to predict future trends. However, compared to the seasonal component, the cyclic component is irregular and difficult to predict. The noise component is unpredictable and occurs randomly. The greater the volume of noise, the more difficult it is to determine patterns and trends of other components. For example, a farmer can use the seasonal component to predict ticket sales to their pumpkin patch. It is predictable that ticket sales will increase in the fall and begin to drop off after thanksgiving and into the winter, until the next fall year when they increase again.


Time series regression is a way to collect data of one single item over time (Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M., 2020). It is different from a simple regression because simple regression focuses on collecting data on an item and observing its performance for the current time rather than forecasting its performance over time. On the other hand, multiple regression requires two or more variables to collect data. With time-series regression, one can forecast how a unit will perform a day, week, month, or years from now. The information gathered from a time series regression is pertinent when tracking the economy’s performance, inflation, weather, cigarette usage per capita, etc. (Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M., 2020). Time series regression requires using the dynamic causal effect, which is the data collected to estimate the impact of Y change to X; over time (Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M., 2020).

A realistic example that I will use to explain a time series regression is weather forecasting. Weather experts use time series regression to study the data from the past and today to foresee how the weather would be tomorrow (Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M., 2020). The model that is used for predicting weather is both a lag model and a vector autoregression model. While the lag model is used to analyze the “correlation between adjacent days and years”, the vector autoregression model is used to depict “historical data” (Liu, Y., Roberts, M.C. & Sioshansi, R., 2018).



Hanck, C. Arnold, M. Gerber, A. and Schmelzer, M. Introduction to Econometrics with R. September 15, 2020. University of Duisburg-Essen

Essen, Germany. Retrieved from: Introduction to Econometrics with R (

Liu, Y., Roberts, M.C. & Sioshansi, R. A vector autoregression weather model for electricity supply and demand modeling. J. Mod. Power Syst. Clean Energy 6,763–776 (2018).

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