... ., et al.: Data augmentation techniques in time series domain: A survey and taxonomy. arXiv preprint arXiv:220613508 (2022) updates 2 5 1 4 Prediction of Deposition Parameters in Optimizing Biomass Forecasting and Supply Chain 71 ...

This is relevant for many applications, such as model predictive control, that requires predicting the values for the whole receding horizon.

... review and tutorial article, “Deep Learning for Time Series Forecasting: Tutorial and Literature Survey”, Benidis et al., ACM Computing Surveys 55(6), 2023, article No.: 12, can be found at https://dl.acm.org/doi/10.1145/3533382. The ...

In addition to that, in this novel research, we modeled the current COVID-19 pandemic using deep learning-based time series modeling.

... machine learning models . J. Hydrol . 587 , 124989 ( 2020 ) 3. Benidis , K. , et al .: Deep learning for time series forecasting : tutorial and literature survey . ACM Comput . Surv . 55 ( 6 ) , 1-36 ( 2022 ) 4. Bi , J. , Zhang , L ...

This book covers the recent advancements in time series forecasting. The book includes theoretical as well as recent applications of time series analysis.

The book is a summary of a time series forecasting competition that was held a number of years ago.

This book is an accessible guide that doesn’t require a background in calculus to be engaging but does not shy away from deeper explanations of the techniques discussed.

In this Book, different normalization methods are used on time series data before feeding the data into the DNN model and we try to find out the impact of each normalization technique on DNN for TSF.