Predicting Computer Network Traffic: A Time Series Forecasting Approach Using DWT, ARIMA and RNN

2018 ◽  
Author(s):  
Rishabh Madan ◽  
Partha Sarathi Mangipudi
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Computer Network ◽  
Time Series Forecasting

scholarly journals Multi-objective Optimization Algorithm for Multimedia English Teaching (MOAMET) Based on Computer Network Traffic Prediction Model

2018 ◽  
Vol 13 (03) ◽  
pp. 58 ◽  
Author(s):  
Wei Wei Feng
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Optimization Algorithm ◽  
Network Traffic ◽  
Computer Network ◽  
Reference Points ◽  
Traffic Prediction ◽  
Multi Objective Optimization ◽  
English Teaching ◽  
Multi Objective

In order to solve the problem of multi-objective optimization for multimedia English teaching, this paper proposes a multi-objective optimization algorithm for multimedia English teaching (MOAMET) based on computer network traffic prediction model, which is based on the computer network traffic prediction model strategy. This algorithm establishes time series for individuals correlated to same reference points, and for such time series through computer network traffic model optimizes multimedia English teaching objectives. Meanwhile, it feeds back the prediction error of the historical moment to the current prediction to improve the accuracy of the optimization, and adds disturbance in each optimized individual to increase the diversity of initial multimedia English teaching so as to speed up the convergence speed of the algorithm in the new environment. Through experiments it teats the algorithm, also makes comparison and analysis with two existing algorithms, the results show that the proposed algorithm can maintain good performance in dealing with multi-objective optimization for multimedia English teaching.

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.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

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