Let’s delve into each category of forecasting methods:

1.Time Series Methods: Time series forecasting methods are specifically designed to handle data points collected over time. They analyze historical data to identify patterns, trends, and seasonality, and then use these insights to forecast future values.

1.ARIMA (Autoregressive Integrated Moving Average): ARIMA models capture linear dependencies in the time series data, including autoregressive (AR) and moving average (MA) components. It’s effective for stationary time series data.

2.SARIMA (Seasonal ARIMA): SARIMA extends ARIMA by incorporating seasonality into the model. It’s suitable for time series data with seasonal patterns.

3.Holt-Winters: Holt-Winters models include components for trend, seasonality, and level in the time series data. They are useful for forecasting data with both trend and seasonal patterns.

4.LSTM (Long Short-Term Memory): LSTM is a type of recurrent neural network (RNN) that is well-suited for capturing complex patterns and long-term dependencies in time series data. It’s particularly effective for sequential data with non-linear patterns.

2.Regression Methods: Regression methods predict a continuous outcome variable based on one or more predictor variables. While traditionally used for cross-sectional data, they can also be adapted for time series forecasting by including lagged variables or other time-related features.

1.Linear Regression: Linear regression models establish a linear relationship between the predictor variables and the target variable. They are simple and easy to interpret but may not capture complex relationships in the data.

2.Decision Tree: Decision tree models partition the data into subsets based on the values of predictor variables, creating a tree-like structure for making predictions. They are capable of capturing non-linear relationships and interactions among variables.

3.Ridge Regression and Lasso Regression: Ridge regression and Lasso regression are regularization techniques used to prevent overfitting in linear regression models. They add penalty terms to the regression coefficients to shrink them towards zero, reducing model complexity and improving generalization performance.

3.Machine Learning (ML) Methods: Machine learning methods leverage algorithms to learn patterns can handle complex, high-dimensional data and are capable of capturing non-linear relationships.

1.Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to make predictions. It’s robust against overfitting and performs well with large datasets and high-dimensional feature spaces.

2.XGBoost (Extreme Gradient Boosting): XGBoost is a gradient boosting algorithm that builds a series of decision trees sequentially, where each tree corrects the errors of the previous ones. It’s known for its speed, scalability, and high predictive accuracy.

3.Prophet: Prophet is an open-source forecasting tool developed by Facebook. It’s designed to handle time series data with multiple seasonality patterns.