Improving Time Series Forecasting Using Mathematical and Deep Learning Models

2018 ◽  
pp. 115-125
Author(s):  
Mohit Gupta ◽  
Ayushi Asthana ◽  
Nishant Joshi ◽  
Pulkit Mehndiratta
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Learning Models

Scaling Deep Learning Models for Large Spatial Time-Series Forecasting

2019 ◽  
Author(s):  
Zainab Abbas ◽  
Jon Reginbald Ivarsson ◽  
Ahmad Al-Shishtawy ◽  
Vladimir Vlassov
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Learning Models

A novel validation framework to enhance deep learning models in time-series forecasting

2020 ◽  
Vol 32 (23) ◽  
pp. 17149-17167
Author(s):  
Ioannis E. Livieris ◽  
Stavros Stavroyiannis ◽  
Emmanuel Pintelas ◽  
Panagiotis Pintelas
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Learning Models ◽  
Validation Framework

scholarly journals Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study

2020 ◽  
Vol 140 ◽  
pp. 110227 ◽  
Author(s):  
Sourabh Shastri ◽  
Kuljeet Singh ◽  
Sachin Kumar ◽  
Paramjit Kour ◽  
Vibhakar Mansotra
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Comparative Case Study ◽  
Learning Models

scholarly journals A Novel Model Based on DA-RNN Network and Skip Gated Recurrent Neural Network for Periodic Time Series Forecasting

2021 ◽  
Vol 14 (1) ◽  
pp. 326
Author(s):  
Bingqing Huang ◽  
Haonan Zheng ◽  
Xinbo Guo ◽  
Yi Yang ◽  
Ximing Liu
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Electricity Consumption ◽  
Predictive Ability ◽  
Time Series Forecasting ◽  
Learning Models ◽  
Long Distance ◽  
Model Based ◽  
Periodic Time Series ◽  
Novel Model

Deep learning models are playing an increasingly important role in time series forecasting with their excellent predictive ability and the convenience of not requiring complex feature engineering. However, the existing deep learning models still have shortcomings in dealing with periodic and long-distance dependent sequences, which lead to unsatisfactory forecasting performance on this type of dataset. To handle these two issues better, this paper proposes a novel periodic time series forecasting model based on DA-RNN, called DA-SKIP. Using the idea of task decomposition, the novel model, based on DA-RNN, GRU-SKIP and autoregressive component, breaks down the prediction of periodic time series into three parts: linear forecasting, nonlinear forecasting and periodic forecasting. The results of the experiments on Solar Energy, Electricity Consumption and Air Quality datasets show that the proposed model outperforms the three comparison models in capturing periodicity and long-distance dependence features of sequences.

scholarly journals A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

2021 ◽  
Vol 5 (4) ◽  
pp. 175
Author(s):  
Ahmed I. Shahin ◽  
Sultan Almotairi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Machine Learning Algorithms ◽  
Forecasting Model ◽  
Learning Models ◽  
Forecasting Models ◽  
Proposed Model ◽  
Death Cases

The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.

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.

Deep learning models for forecasting aviation demand time series

2021 ◽  
Author(s):  
Andreas Kanavos ◽  
Fotios Kounelis ◽  
Lazaros Iliadis ◽  
Christos Makris
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Learning Models
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