Application of time series model and deep learning method in measuring the impact of COVID-19 on agriculture in Hubei, China

In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

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Acoustic Emission Signal Classification Using Feature Analysis and Deep Learning Neural Network

Fluctuation and Noise Letters ◽

10.1142/s0219477521500309 ◽

2020 ◽

pp. 2150030

Author(s):

Jian-Da Wu ◽

Yu-Han Wong ◽

Wen-Jun Luo ◽

Kai-Chao Yao

Keyword(s):

Acoustic Emission ◽

Deep Learning ◽

Frequency Domain ◽

Acoustic Emission Signal ◽

Signal Classification ◽

Learning Method ◽

Identification Rate ◽

Emission Signal ◽

Identification Time ◽

The Impact

With the development of artificial intelligence in recent years, deep learning has been widely used in mechanical system signal classification but the impact of different feature extractions on the efficiency and effectiveness of deep learning neural networks is more important. In this study, a vehicle classification based on engine acoustic emission signal in the time domain, the frequency domain and the wavelet transform domain for deep learning network techniques is presented and compared. In signal classification, different feature extractions will show in different decomposition levels and can be used to recognize the various acoustic conditions. In the experimental work, as engines from 10 different ground vehicles operate, the measured sound signal is converted into a digital signal, and the established data set is classified and identified by the deep learning method. The number of samples, identification rate and identification time in the various signal domains are compared and discussed in this study. Finally, the experimental results and data analysis show that by using the wavelet signal and the deep learning method, excellent identification time and identification rate can be achieved, compared with traditional time and frequency domain signals.

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Poster abstract: Traffic flow prediction with big data: A deep learning based time series model

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) ◽

10.1109/infcomw.2017.8116535 ◽

2017 ◽

Cited By ~ 11

Author(s):

Yuanfang Chen ◽

Lei Shu ◽

Lei Wang

Keyword(s):

Time Series ◽

Big Data ◽

Deep Learning ◽

Traffic Flow ◽

Time Series Model ◽

Poster Abstract ◽

Traffic Flow Prediction ◽

Flow Prediction

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Determining the impact of window length on time series forecasting using deep learning

International Journal of Advanced Computer Research ◽

10.19101/ijacr.pid77 ◽

2019 ◽

Vol 9 (44) ◽

pp. 260-267

Author(s):

Ammar Azlan ◽

Yuhanis Yusof ◽

Mohamad Farhan Mohamad Mohsin

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Forecasting ◽

Window Length ◽

The Impact

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Bi-LSTM based deep learning method for 5G signal detection and channel estimation

AIMS Electronics and Electrical Engineering ◽

10.3934/electreng.2021017 ◽

2021 ◽

Vol 5 (4) ◽

pp. 334-341

Author(s):

D Venkata Ratnam ◽

◽

K Nageswara Rao ◽

Keyword(s):

Deep Learning ◽

Wireless Communication ◽

Channel Estimation ◽

Signal Detection ◽

Communication Systems ◽

Estimation Method ◽

Estimation Methods ◽

Delay Spread ◽

Learning Method ◽

The Impact

<abstract> <p>The advanced neural network methods solve significant signal estimation and channel characterization difficulties in the next-generation 5G wireless communication systems. The number of transmitted signal copies received through multiple paths at the receiver leads to delay spread, which intern causes interference in communication. These adverse effects of the interference can be mitigated with the orthogonal frequency division modulation (OFDM) technique. Furthermore, the proper signal detection methods optimal channel estimation enhances the performance of the multicarrier wireless communication system. In this paper, bi-directional long short-term memory (Bi-LSTM) based deep learning method is implemented to estimate the channel in different multipath scenarios. The impact of the pilots and cyclic prefix on the performance of Bi LSTM algorithm is analyzed. It is evident from the symbol-error rate (SER) results that the Bi-LSTM algorithm performs better than the state of art channel estimation methods known as the Minimum Mean Square and Error (MMSE) estimation method.</p> </abstract>

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Intraday time series load forecasting using Bayesian deep learning method—a new approach

Electrical Engineering ◽

10.1007/s00202-021-01411-5 ◽

2021 ◽

Author(s):

D. Kiruthiga ◽

V. Manikandan

Keyword(s):

Time Series ◽

Deep Learning ◽

Load Forecasting ◽

Learning Method ◽

New Approach

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A novel hybrid deep learning model for taxi demand forecasting based on decomposition of time series and fusion of text data

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210657 ◽

2021 ◽

pp. 1-17

Author(s):

Kun Zhu ◽

Shuai Zhang ◽

Wenyu Zhang ◽

Zhiqiang Zhang

Keyword(s):

Time Series ◽

Deep Learning ◽

Demand Forecasting ◽

Learning Model ◽

Seasonal Trend ◽

Text Data ◽

Optimal Weights ◽

Frequent Event ◽

The Impact ◽

Deep Learning Model

Accurate taxi demand forecasting is significant to estimate the change of demand to further make informed decisions. Although deep learning methods have been widely applied for taxi demand forecasting, they neglect the complexity of taxi demand data and the impact of event occurrences, making it hard to effectively model the taxi demand in highly dynamic areas (e.g., areas with frequent event occurrences). Therefore, to achieve accurate and stable taxi demand forecasting in highly dynamic areas, a novel hybrid deep learning model is proposed in this study. First, to reduce the complexity of taxi demand time series, the seasonal-trend decomposition procedures based on loess is employed to decompose the time series into three simpler components (i.e., seasonal, trend, and remainder components). Then, different forecasting methods are adopted to handle different components to obtain robust forecasting results. Moreover, considering the instability and nonlinearity of the remainder component, this study proposed to fuse the event features (in particular, text data) to capture the unusual fluctuation patterns of remainder component and solve its extreme value problem. Finally, genetic algorithm is applied to determine the optimal weights for integrating the forecasting results of three components to obtain the final taxi demand. The experimental results demonstrate the better accuracy and reliability of the proposed model compared with other baseline forecasting models.

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Prediction of Interbank Offered Rate Based on Time Series Model

E3S Web of Conferences ◽

10.1051/e3sconf/202125101014 ◽

2021 ◽

Vol 251 ◽

pp. 01014

Author(s):

Ding Huang ◽

Ming Zhong ◽

Xupeng Shi

Keyword(s):

Decision Making ◽

Time Series ◽

Time Series Data ◽

Time Series Model ◽

Grey Model ◽

Actual Data ◽

Series Data ◽

Bank Management ◽

Working Day ◽

The Impact

This paper studies the prediction of interbank offered rate changes in each working day. Using the actual data of each working day of China’s interbank offered rate from 2007 to 2019, this paper sets up ARIMA, Prophet, grey model and MTGNN to study and verify the time series data, and make a comparison between these models. The limitation of this paper is that it does not consider the impact of macroeconomic characteristics but only considers the predict changes in time series. The results of this paper are expected to be helpful for bank management and interbank transaction decision making.

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Study of the impact of the COVID-19 pandemic on international air transportation

Discrete and Continuous Models and Applied Computational Science ◽

10.22363/2658-4670-2021-29-1-22-35 ◽

2021 ◽

Vol 29 (1) ◽

pp. 22-35

Author(s):

Eugeny Yu. Shchetinin

Keyword(s):

Neural Networks ◽

Time Series ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Stock Prices ◽

Short Term Memory ◽

Air Transportation ◽

Time Series Forecasting ◽

The Cost ◽

The Impact

Time Series Forecasting has always been a very important area of research in many domains because many different types of data are stored as time series. Given the growing availability of data and computing power in the recent years, Deep Learning has become a fundamental part of the new generation of Time Series Forecasting models, obtaining excellent results.As different time series problems are studied in many different fields, a large number of new architectures have been developed in recent years. This has also been simplified by the growing availability of open source frameworks, which make the development of new custom network components easier and faster.In this paper three different Deep Learning Architecture for Time Series Forecasting are presented: Recurrent Neural Networks (RNNs), that are the most classical and used architecture for Time Series Forecasting problems; Long Short-Term Memory (LSTM), that are an evolution of RNNs developed in order to overcome the vanishing gradient problem; Gated Recurrent Unit (GRU), that are another evolution of RNNs, similar to LSTM.The article is devoted to modeling and forecasting the cost of international air transportation in a pandemic using deep learning methods. The author builds time series models of the American Airlines (AAL) stock prices for a selected period using LSTM, GRU, RNN recurrent neural networks models and compare the accuracy forecast results.

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The Causal Impact of the Rapid Czech Interest Rate Hike on the Czech Exchange Rate Assessed by the Bayesian Structural Time Series Model

10.52950/es.2021.10.2.001 ◽

2021 ◽

Vol 10 (2) ◽

pp. 1-17

Author(s):

Ondrej Bednar

Keyword(s):

Time Series ◽

Exchange Rate ◽

Interest Rate ◽

Central Bank ◽

Causal Effect ◽

Time Series Model ◽

Structural Time Series ◽

Causal Impact ◽

Structural Time ◽

The Impact

I have employed the Bayesian Structural Time Series model to assess the recent interest rate hike by the Czech Central Bank and its causal impact on the Koruna exchange rate. By forecasting exchange rate time series in the absence of the intervention we can subtract the observed values from the prediction and estimate the causal effect. The results show that the impact was little and time limited in one model specification and none in the second version. It implies that the Czech Central Bank possesses the ability to diverge significantly from the Eurozone benchmark interest rate at least in the short term. It also shows that the interest rate hike will not be able to curb global inflation forces on the domestic price level.

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