Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality.

About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library.

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods.

This book constitutes revised selected papers from the 5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020, in Ghent, Belgium, in September 2020.* The 8 full and 3 short papers ...

This book demystifies the technique, providing readers with little or no time series or machine learning experience the fundamental tools required to create and evaluate time series models.

The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems.

... trained model. Next Sentence Prediction is to learn the relationships of different time series by judging whether two time series fragments are adjacent ... Time Series Anomaly Detection via Pre-training Model Bert 213 3.2 Pre-training.

Forecasting is required in many situations.

... trained by predicting the matching pairs from a set of query - reference examples . At discord search phase , anomaly scores are calculated as the similarity between the embeddings of query and ... Time Series Anomaly Detection.

... learning is a machine learning technique where a model trained on one task (a source domain) is re-purposed on a second related task (a target domain). Transfer learning is popular in deep learning, including Convolutional Neural ...