Long-term time series forecasting

Information

• Long-term time series forecasting refers to the prediction of a time series for a horizon that is typically greater than a year.
• This type of forecasting is often used in business and economics, such as for predicting stock prices, interest rates, and sales data for a company.
• Long-term time series forecasting can be challenging due to the high number of variables that can affect the outcome and the difficulty in predicting long-term trends.
• To tackle this problem, various machine learning and statistical models can be used, such as ARIMA, SARIMA, LSTM, and neural networks.
• One of the challenges of long-term forecasting is selecting the appropriate horizon and granularity, i.e., how far into the future should the forecast be made and at what frequency should the data be collected and analyzed.
• Long-term forecasting also requires a high degree of accuracy and reliability, as it can have significant implications for decision-making and resource allocation.
• The performance of long-term forecasting models can be evaluated using various metrics, such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE).
• In general, long-term time series forecasting requires a thorough understanding of the underlying data, the problem at hand, and the appropriate modeling techniques to be used.