Danna, one of our data scientists, explains when to use regression vs. time series, a subset of regression, for machine learning problems you want to solve.
A question you may have when it comes down to using time series and regression is which one should I use to solve my machine learning problem?One thing that may be confusing is that having a time feature does not necessarily mean you have a time series problem.
For example, you may have a data set of house prices with features describing the houses including the year that house was built. Even though you have a date as a feature, this is not a time series problem. In time series forecasting, we are generally interested in predicting something that is changing over time, but in this data set, we have several different houses with one date and will be predicting the prices of other houses. So, this is a regression problem.
For example, you may have a data set of house prices with features describing the houses including the year that house was built. Even though you have a date as a feature, this is not a time series problem.
Another thing that may tell you that your problem is regression and not time series is if there isn’t really a relationship with your target and time.
In time series problems, we expect observations close to each other in time to be more similar than observations far away, after accounting for seasonality. For example, the weather today is usually more similar to the weather tomorrow than the weather a month from now. So, predicting the weather based on past weather observations is a time series problem.
FAQs
Time series is more suitable for forecasting and detecting patterns in temporal data, while regression is more suitable for estimating and explaining the effect of variables on an outcome.
Why linear regression is not suitable for time series? ›
Time series data is inherently sequential, with each observation depending on the previous ones. Linear regression models, on the other hand, do not consider the time dependency and assume that the observations are independent.
What is the best regression model for time series data? ›
The most common method used for time series regression analysis is ordinary least squares (OLS) regression. The software will estimate the coefficients of the model, which represent the strength and direction of the relationship between the dependent and independent variables.
How do you evaluate a time series regression model? ›
Key metrics for evaluating a time series forecasting model include Mean Absolute Error (MAE) for average absolute errors, Root Mean Squared Error (RMSE) to highlight larger errors, Mean Absolute Percentage Error (MAPE) for error in percentage terms, R-squared (R²) for the variance explained by the model, and Forecast ...
What is the difference between time series and multiple regression? ›
3 Differences and similarities
Time series analysis focuses on how a single variable changes over time, while regression analysis focuses on how multiple variables interact with each other. Time series data are arranged in chronological order, while regression data are not necessarily ordered.
What is the first difference of a time series regression? ›
The first difference of a time series is the series of changes from one period to the next. If Yt denotes the value of the time series Y at period t, then the first difference of Y at period t is equal to Yt-Yt-1. In Statgraphics, the first difference of Y is expressed as DIFF(Y), and in RegressIt it is Y_DIFF1.
What are the limitations of time series regression analysis? ›
Disadvantages of time series analysis
It can suffer from generalization from a single study where more data points and models were warranted. Human error could misidentify the correct data model, which can have a snowballing effect on the output. It could also be difficult to obtain the appropriate data points.
Why is regression better than time series? ›
Business and macroeconomic times series often have strong contemporaneous correlations, but significant leading correlations--i.e., cross-correlations with other variables at positive lags--are often hard to find. Thus, regression models may be better at predicting the present than the future.
What is the difference between time series regression and linear regression? ›
A time series regression forecasts a time series as a linear relationship with the independent variables. The linear regression model assumes there is a linear relationship between the forecast variable and the predictor variables.
What is basic assumption of time series regression? ›
Regression Assumptions
There is a linear relationship between the independent variable(s) and the dependent variable. All the variables are normally distributed; to check, plot a histogram of the residuals.
If you have time-based data that exhibits trend, seasonality, and cyclic patterns, then Time Series Analysis is the best statistical technique to use. If you have independent data that does not exhibit a time-based relationship among the data points, then Regression Analysis is the best statistical technique to use.
Can you use multiple regression on time series? ›
Multiple linear regression models assume that a response variable is a linear combination of predictor variables, a constant, and a random disturbance. If the variables are time series processes, then classical linear model assumptions, such as spherical disturbances, might not hold.
What is the best algorithm for time series forecasting? ›
Autoregressive Integrated Moving Average (ARIMA) is a commonly used statistical algorithm for time-series forecasting. The algorithm is especially useful for simple datasets with under 100 time series.
Can you use regression for time series? ›
Time series regression can help you understand and predict the behavior of dynamic systems from experimental or observational data. Common uses of time series regression include modeling and forecasting of economic, financial, biological, and engineering systems.
What is the main use of regression analysis in the time series analysis? ›
It differs from classification models because it estimates a numerical value, whereas classification models identify which category an observation belongs to. The main uses of regression analysis are forecasting, time series modeling and finding the cause and effect relationship between variables.
What is the difference between time series and panel regression? ›
Time series data means that we have data from one unit, over many points in time. Panel data (or time series cross section) means that we have data from many units, over many points in time.
What is the difference between time series and regression as methods used for forecasting? ›
Regression models are used to predict a value of a dependent variable (Y) from an independent variable (X). These models assume that the relationship between X and Y is linear. Time-series models are used to predict future values of a dependent variable (Y) from its past values (X).
What is the main difference between regression and classification problem? ›
The key distinction between Classification vs Regression algorithms is Regression algorithms are used to determine continuous values such as price, income, age, etc. and Classification algorithms are used to forecast or classify the distinct values such as Real or False, Male or Female, Spam or Not Spam, etc.
