Time series forecasting is the task of predicting future values of a time series (as well as uncertainty bounds).
( Image credit: DTS )
A network-based Transfer Learning approach for deep neural networks is designed to investigate the efficiency of Transfer Learning in the domain of food sales forecasting.
The motivation of the framework is to either transform existing highly accurate point forecast models to their probabilistic counterparts or to train GANs stably by selecting the architecture of GAN's component carefully and efficiently.
Numerous deep learning architectures have been developed to accommodate the diversity of time series datasets across different domains.
Unfortunately, current self-adaptive approaches do not account for tactic volatility in their decision-making processes, and merely assume that tactics do not experience volatility.
Application of exponentially smoothed RNNs to electricity load forecasting, weather data and financial time series, such as minute level Bitcoin prices and CME futures tick data, highlight the efficacy of exponential smoothing for multi-step time series forecasting.
Generative Adversarial Networks (GANs) have gained significant attention in recent years, with particularly impressive applications highlighted in computer vision.
In this paper, we improve the memory-augmented RNN with important architectural and state updating mechanisms that ensure that the model learns to properly balance the use of its latent states with external memory.
We participated in the M4 competition for time series forecasting and describe here our methods for forecasting daily time series.