Neural networks can generate various outputs, which is especially useful in time-series forecasting to forecast longer periods. In time series regression, the number of neurons in the final output layer determines how many steps in a time series the model can predict. Models with one output return single-step forecasts. Models … Read more
ARIMA (Auto-Regressive Integrated Moving Average) is a statistical modeling technique for time series analysis and forecasting. Compared to machine learning, ARIMA is a classical modeling approach that is particularly powerful when the time series being analyzed follows a clear pattern. This is, for example, the case when the time series … Read more
Evaluating performance is a crucial step in developing regression models. Because regression models return continuous outputs, such models allow for different gradations of right or wrong. Therefore, we measure the deviation between predictions and actual values in numerical terms. However, a universal metric to measure the performance of regression models … Read more