scholarly journals Mixing ARMA Models with EGARCH Models and Using it in Modeling and Analyzing the Time Series of Temperature

2021 ◽  
pp. 2307-2326
Author(s):  
Abduljabbar Ali Mudhir
Keyword(s):  
Time Series ◽  
Mixed Model ◽  
Time Series Data ◽  
Series Data ◽  
Data Series ◽  
Arma Models ◽  
Prediction Ability ◽  
Proposed Model ◽  
Egarch Model ◽  
Unconditional Variance

In this article our goal is mixing ARMA models with EGARCH models and composing a mixed model ARMA(R,M)-EGARCH(Q,P) with two steps, the first step includes modeling the data series by using EGARCH model alone interspersed with steps of detecting the heteroscedasticity effect and estimating  the model's parameters and check the adequacy of the model. Also we are predicting the conditional variance and verifying it's convergence to the unconditional variance value. The second step includes mixing ARMA with EGARCH and using the mixed (composite) model in modeling time series data and predict future values then asses the prediction ability of the proposed model by using prediction error criterions.

An Efficient Method for Forecasting Using Fuzzy Time Series

2017 ◽  
pp. 287-304 ◽  
Author(s):  
Pritpal Singh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Data Sets ◽  
Proposed Model ◽  
Temperature Forecasting ◽  
The University

Forecasting using fuzzy time series has been applied in several areas including forecasting university enrollments, sales, road accidents, financial forecasting, weather forecasting, etc. Recently, many researchers have paid attention to apply fuzzy time series in time series forecasting problems. In this paper, we present a new model to forecast the enrollments in the University of Alabama and the daily average temperature in Taipei, based on one-factor fuzzy time series. In this model, a new frequency based clustering technique is employed for partitioning the time series data sets into different intervals. For defuzzification function, two new principles are also incorporated in this model. In case of enrollments as well daily temperature forecasting, proposed model exhibits very small error rate.

scholarly journals Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

2020 ◽  
Vol 12 (17) ◽  
pp. 2735 ◽  
Author(s):  
Carlos M. Souza ◽  
Julia Z. Shimbo ◽  
Marcos R. Rosa ◽  
Leandro L. Parente ◽  
Ane A. Alencar ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Google Earth ◽  
Series Data ◽  
Data Series ◽  
Forest Conversion ◽  
Pixel Resolution ◽  
Historical Land Use

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

A self-adapting multi-LSTM ensemble regression mode for failure prediction of transmission line network from wireless mesh nodes’ data

2020 ◽  
pp. 1-9
Author(s):  
Hongbin Sun ◽  
Mingjun Liu ◽  
Zhejun Qing ◽  
Chandler Miller
Keyword(s):  
Time Series ◽  
Transmission Line ◽  
Time Series Data ◽  
Failure Prediction ◽  
Series Data ◽  
Good Prediction ◽  
Similar Time ◽  
Prediction Ability ◽  
Wireless Mesh ◽  
Ensemble Regression

Transmission lines’ condition monitoring is an important part of smart grid construction. To ensure fast and efficient transmission of data, many mash-based wireless networks devices are adopted to collect status information. Since these nodes are exposed to the natural environment, vulnerable to damage, so it is very necessary to be predicting nodes’ fault. However, these mesh nodes are affected by a variety of complex and time-series factors, and traditional models are difficult to achieve effective failure prediction. To solve this problem, this paper proposes a self-adapting multi-LSTM ensemble regression model for transmission line network’s wireless mesh node failure prediction (MLSTM-FP), through establishes the corresponding relationship between similar time factors and LSTMs, the proposed model can realize multi time series data self-adapting and accurate failure prediction of transmission line network’s wireless mesh nodes, The experimental results show that the proposed method has a good prediction ability than traditional methods.

Time Series Data Mining Implemented on Football Match

2010 ◽  
Vol 26-28 ◽  
pp. 98-103 ◽  
Author(s):  
Ben Cheng Chai
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Data Mining Technique ◽  
Time Series Data Mining ◽  
Proposed Model ◽  
Interesting Pattern ◽  
Football Match ◽  
The One

This study utilizes time series data mining to find the interesting pattern and cooperation custom. Meanwhile, data mining technique and some special football skills such as ball possession are employed to build a novel decision model in football match. The proposed model is expatiated through real football match. In short, on the one hand, the model provides a feasible route to guide the decision makers including football coach to establish effective mechanism in football match. On the other hand, it extends the application scope of time series data mining.

scholarly journals Optimization Algorithm of Tourism Security Early Warning Information System Based on Long Short-Term Memory (LSTM)

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Lei Feng ◽  
Yukai Hao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Information System ◽  
Optimization Algorithm ◽  
Early Warning ◽  
Time Series Data ◽  
Tourism Industry ◽  
Series Data ◽  
Prediction Ability ◽  
Analyze Data

Tourism safety is the focus of the tourism industry. It is not only related to the safety of tourists’ lives and property, but also related to social stability and sustainable development of the tourism industry. However, the security early warning of many scenic spots focuses on the response measures and remedial plans after the occurrence of security incidents, and the staff of many scenic spots have limited security awareness and information analysis ability, which is prone to lag in information release, and do not pay attention to the information of potential security problems. Therefore, this paper studies the optimization algorithm of the tourism security early warning information system based on the LSTM model and uses the recurrent neural network and LSTM to improve the processing and prediction ability of time-series data. The experimental results show that the number of three hidden layers in the tourism security early warning information system based on the LSTM model can reduce the training time of the model and improve the performance. Compared with the tourism safety early warning information system based on the BP neural network, it has better accuracy and stability, has better processing and prediction ability for time series data, and can monitor and analyze data scientifically in real-time and dynamically analyze data.

scholarly journals A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

2021 ◽  
Vol 7 ◽  
pp. e534
Author(s):  
Kristoko Dwi Hartomo ◽  
Yessica Nataliani
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Forecasting ◽  
Series Data ◽  
Improvement Rate ◽  
New Model ◽  
Average Improvement ◽  
Proposed Model

This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

scholarly journals An ECM Approach for Long Run Relationship Between Real Exchange Rate and Output Growth: Evidence from Bangladesh

2015 ◽  
Vol 63 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Khnd Md Mostafa Kamal
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Real Exchange Rate ◽  
Time Series Data ◽  
Output Growth ◽  
Series Data ◽  
Data Series ◽  
Currency Exchange ◽  
Long Run ◽  
Short Run

Currency exchange rate is an important aspect in modern economy which indicates the strength of domestic currency with respect to international currency. This study uses 42 years’ (1972 to 2013) time series data for Bangladesh in order to empirically determine whether the real exchange rate has significant impact on output growth for Bangladesh by using error correction model (ECM).The time series econometrics properties of the data series have been thoroughly investigated to apply ECM approach. The empirical evidence suggests mixed results; in the short run low exchange rate has positive significant effect while in the long run output growth is positively affected high exchange rate pass through.Dhaka Univ. J. Sci. 63(2):105-110, 2015 (July)

A quantum approach for time series data based on graph and Schrödinger equations methods

2018 ◽  
Vol 33 (35) ◽  
pp. 1850208 ◽  
Author(s):  
Pritpal Singh ◽  
Gaurav Dhiman ◽  
Amandeep Kaur
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Error Rates ◽  
Series Data ◽  
Quantum Model ◽  
Effective Solution ◽  
Proposed Model ◽  
Quantum Approach ◽  
Speed Up ◽  
Competing Models

The supremacy of quantum approach is able to solve the problems which are not practically feasible on classical machines. It suggests a significant speed up of the simulations and decreases the chance of error rates. This paper introduces a new quantum model for time series data which depends on the appropriate length of intervals. To provide effective solution of this problem, this study suggests a new graph-based quantum approach. This technique is useful in discretization and representation of logical relationships. Then, we divide these logical relations into various groups to obtain efficient results. The proposed model is verified and validated with various approaches. Experimental results signify that the proposed model is more precise than existing competing models.

Sign in / Sign up

Export Citation Format

Share Document