When it comes to forecasting, there are several methods that have proven to be reliable and have stood the test of time. These methods have been used for many years and continue to be widely utilized due to their effectiveness. Here are some forecasting methods that have demonstrated longevity and reliability:

1. Moving Averages: Moving averages are a simple yet effective forecasting technique. They involve calculating the average of a specific number of past data points and using it to predict future values. Moving averages are commonly used to identify trends and eliminate short-term fluctuations in data.
2. Exponential Smoothing: Exponential smoothing is a popular forecasting method that assigns exponentially decreasing weights to past observations. It places more emphasis on recent data points while giving less importance to older data. Exponential smoothing is particularly useful when the data exhibits a trend or seasonality.
3. Time Series Analysis: Time series analysis is a comprehensive approach to forecasting that takes into account the patterns and dependencies in historical data. It involves examining factors such as trend, seasonality, and cyclicality to make predictions. Time series models, such as ARIMA (Autoregressive Integrated Moving Average), are commonly used for this purpose.
4. Regression Analysis: Regression analysis is a statistical method used to establish relationships between independent variables and a dependent variable. It is useful for forecasting when there is a clear relationship between the variables. By fitting a regression model to historical data, future values of the dependent variable can be predicted based on the values of the independent variables.
5. Judgmental Forecasting: Judgmental forecasting relies on the expertise and judgment of individuals or panels of experts. It involves gathering insights and opinions from knowledgeable individuals who have experience in the subject matter. This method is particularly valuable when there is limited historical data or when the future is uncertain.
6. Ensemble Forecasting: Ensemble forecasting combines the predictions of multiple forecasting models to generate a more accurate and robust forecast. By aggregating the forecasts from different methods or models, ensemble forecasting reduces the risk of relying on a single approach.

It is important to note that the choice of forecasting method depends on the nature of the data, the specific problem at hand, and the available resources. Different methods may perform better in different situations, and it is often beneficial to combine multiple methods or use a hybrid approach to achieve more accurate forecasts.