scholarly journals DeepLSF: Fusing Knowledge and Data for Time Series Forecasting

2021 ◽  
Author(s):  
Muhammad Ali Chattha
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Real World ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Data Driven

This work presents DeepLSF, a framework for time series forecasting that fuses knowledge driven techniques with data driven neural networks. The proposed framework achieves State-Of-The-Art results on three different real world time series forecasting datasets.

scholarly journals DeepLSF: Fusing Knowledge and Data for Time Series Forecasting

2021 ◽  
Author(s):  
Muhammad Ali Chattha
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Real World ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Data Driven

This work presents DeepLSF, a framework for time series forecasting that fuses knowledge driven techniques with data driven neural networks. The proposed framework achieves State-Of-The-Art results on three different real world time series forecasting datasets.

Data-Driven Time Series Forecasting for Social Studies Using Spatio-Temporal Graph Neural Networks

2021 ◽  
Author(s):  
Yi-Fan Li ◽  
Bo Dong ◽  
Latifur Khan ◽  
Bhavani Thuraisingham ◽  
Patrick T. Brandt ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Social Studies ◽  
Time Series Forecasting ◽  
Data Driven ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Graph Neural Networks

scholarly journals App Download Forecasting: An Evolutionary Hierarchical Competition Approach

2017 ◽  
Author(s):  
Yingzi Wang ◽  
Nicholas Jing Yuan ◽  
Yu Sun ◽  
Chuan Qin ◽  
Xing Xie
Keyword(s):  
Time Series ◽  
Future Development ◽  
Real World ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Comprehensive Understanding ◽  
Competition Analysis ◽  
Multi Scale ◽  
Multi Level ◽  
Product Sales

Product sales forecasting enables comprehensive understanding of products' future development, making it of particular interest for companies to improve their business, for investors to measure the values of firms, and for users to capture the trends of a market. Recent studies show that the complex competition interactions among products directly influence products' future development. However, most existing approaches fail to model the evolutionary competition among products and lack the capability to organically reflect multi-level competition analysis in sales forecasting. To address these problems, we propose the Evolutionary Hierarchical Competition Model (EHCM), which effectively considers the time-evolving multi-level competition among products. The EHCM model systematically integrates hierarchical competition analysis with multi-scale time series forecasting. Extensive experiments using a real-world app download dataset show that EHCM outperforms state-of-the-art methods in various forecasting granularities.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Time Series Forecasting of Cyanobacteria Blooms in the Crestuma Reservoir (Douro River, Portugal) Using Artificial Neural Networks

2006 ◽  
Vol 38 (2) ◽  
pp. 227-237 ◽  
Author(s):  
Luis Oliva Teles ◽  
Vitor Vasconcelos ◽  
Luis Oliva Teles ◽  
Elisa Pereira ◽  
Martin Saker ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Forecasting ◽  
Cyanobacteria Blooms ◽  
Artificial Neural ◽  
Douro River
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