Augmented Out-of-sample Comparison Method for Time Series Forecasting Techniques | AISC
For slides and more information on the paper, visit https://ai.science/e/augmented-out-of...
Speaker: Igor Ilic; Host: Ozan Ozyegen
Motivation:
Time series forecasting contains many practical applications of machine learning. With decades of research, so many algorithms have been created for different purposes. From classic algorithms like ARIMA to contemporary deep recurrent neural network algorithms, deciding which algorithm to use is a complex, resource-intensive process. Join us and take a look at a new, efficient time series model comparison algorithm.