Regression

Linear regression is a fundamental algorithm used for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input features and the target variable.

Linear regression is a fundamental supervised learning algorithm used for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the independent variables and the dependent variable, meaning the change in the output is proportional to the change in inputs. Linear regression models are easy to interpret and computationally efficient, making them popular for many practical applications. However, they are limited in capturing complex patterns in data where relationships are not linear. Despite this, linear regression serves as a strong baseline and is widely used for trend estimation, forecasting, and understanding variable impacts.

Non-linear regression is a type of regression analysis in which the relationship between the independent variable(s) and the dependent variable is modeled as a non-linear function. This allows for capturing complex relationships that cannot be adequately described by linear models.

Non-linear regression models the relationship between input features and the target variable using nonlinear functions. Unlike linear regression, it can capture more complex and intricate patterns in data where the relationship is not simply proportional. These models are powerful for real-world scenarios where data behavior is more complicated, but they are often harder to interpret and require more computational resources. Non-linear regression includes techniques like support vector regression with nonlinear kernels, decision tree regression, and neural networks. Choosing the right non-linear model depends on the data structure and the problem complexity.

Time series forecasting is a technique used to predict future values based on previously observed values. It is widely used in various fields such as finance, economics, and environmental science to analyze trends and make informed decisions.

Time series forecasting involves predicting future values based on previously observed temporal data points. Unlike standard regression, time series methods explicitly account for the order and dependencies in the data over time. Techniques like ARIMA model the linear relationships and trends within time series, while more advanced models like LSTM (Long Short-Term Memory networks) can capture complex, nonlinear temporal patterns and long-term dependencies. Time series forecasting is widely used in domains where future planning and trend prediction are crucial. Challenges include handling seasonality, trends, and noise in the data.