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. We can design neural network architectures with multiple outputs in time-series forecasting to forecast more … 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. … 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, … Read more
Predicting the price of financial assets has fascinated researchers and analysts for many decades. While the traditional prediction methods of technical analysis and fundamental analysis are still widely used, interest is increasingly turning to machine-generated predictions based on deep learning. … Read more