scholarly journals Study of Railway Track Irregularity Standard Deviation Time Series Based on Data Mining and Linear Model

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wei Lili ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Time Series Data ◽  
Arma Model ◽  
Railway Track ◽  
Series Data ◽  
Railway Transportation ◽  
Track Geometry ◽  
Track Irregularity ◽  
Railway Passenger

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

scholarly journals Track Irregularity Time Series Analysis and Trend Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wang Futian ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Measuring Point ◽  
Trend Forecasting ◽  
Changing Trend ◽  
Track Irregularity ◽  
Linear Differential

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

scholarly journals Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
Md. Rabiul Islam ◽  
Md. Rashed-Al-Mahfuz ◽  
Shamim Ahmad ◽  
Md. Khademul Islam Molla
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Financial Time Series ◽  
Arma Model ◽  
Series Data ◽  
Discrete Wavelet ◽  
Multivariate Empirical Mode Decomposition ◽  
Financial Time ◽  
Mode Decomposition

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

scholarly journals Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Chaolong Jia ◽  
Lili Wei ◽  
Hanning Wang ◽  
Jiulin Yang
Keyword(s):  
Time Series ◽  
Wavelet Decomposition ◽  
Time Series Data ◽  
Series Data ◽  
Correction Algorithm ◽  
Data Sequence ◽  
Variation Pattern ◽  
Offset Correction ◽  
Changing Trend ◽  
Track Irregularity

Focusing on problems existing in track irregularity time series data quality, this paper first presents abnormal data identification, data offset correction algorithm, local outlier data identification, and noise cancellation algorithms. And then proposes track irregularity time series decomposition and reconstruction through the wavelet decomposition and reconstruction approach. Finally, the patterns and features of track irregularity standard deviation data sequence in unit sections are studied, and the changing trend of track irregularity time series is discovered and described.

scholarly journals Forecasting of Tomato Yields Using Attention-Based LSTM Network and ARMA Model

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1576
Author(s):  
Wanhyun Cho ◽  
Sangkyuoon Kim ◽  
Myunghwan Na ◽  
Inseop Na
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Environmental Factors ◽  
Environmental Variables ◽  
Time Series Data ◽  
Arma Model ◽  
Series Data ◽  
Proposed Model ◽  
Lstm Network ◽  
Statistical Time

Nonlinear autoregressive exogenous (NARX), autoregressive integrated moving average (ARIMA) and multi-layer perceptron (MLP) networks have been widely used to predict the appearance value of future points for time series data. However, in recent years, new approaches to predict time series data based on various networks of deep learning have been proposed. In this paper, we tried to predict how various environmental factors with time series information affect the yields of tomatoes by combining a traditional statistical time series model and a deep learning model. In the first half of the proposed model, we used an encoding attention-based long short-term memory (LSTM) network to identify environmental variables that affect the time series data for tomatoes yields. In the second half of the proposed model, we used the ARMA model as a statistical time series analysis model to improve the difference between the actual yields and the predicted yields given by the attention-based LSTM network at the first half of the proposed model. Next, we predicted the yields of tomatoes in the future based on the measured values of environmental variables given during the observed period using a model built by integrating the two models. Finally, the proposed model was applied to determine which environmental factors affect tomato production, and at the same time, an experiment was conducted to investigate how well the yields of tomatoes could be predicted. From the results of the experiments, it was found that the proposed method predicts the response value using exogenous variables more efficiently and better than the existing models. In addition, we found that the environmental factors that greatly affect the yields of tomatoes are internal temperature, internal humidity, and CO2 level.

scholarly journals Pakistan management of green transportation and environmental pollution: a nonlinear ARDL analysis

2021 ◽  
Author(s):  
Muhammad Tayyab Sohail ◽  
Sana Ullah ◽  
Muhammad Tariq Majeed ◽  
Ahmed Usman
Keyword(s):  
Environmental Pollution ◽  
Linear Models ◽  
Time Series Data ◽  
Nonlinear Models ◽  
Nonlinear Modeling ◽  
Series Data ◽  
Railway Transportation ◽  
Long Run ◽  
Railway Passenger ◽  
Positive Shock

AbstractModern advances in nonlinear modeling have exposed that nonlinear models yield more robust results compared with linear models. Research on the effect of air-railway transportation on environmental pollution has now arrived into a new way of asymmetry analysis and captured the real issue among the nexus. This study aims to inspect the asymmetric impact of air-railway transportation on environmental pollution in Pakistan by using annual time series data from 1991 to 2019. The findings show that positive shock in air passenger carried and railway passenger carried increases carbon emissions, which implies that 1% increase in air passenger carried (railway passenger carried) enhances environmental pollution by 0.21% (0.32%) in long run in Pakistan. While positive shock in railway passengers carried increases environmental pollution and negative shock in railway passengers carried decreases the environmental pollution in the short run. The outcomes have also confirmed the short- and long-run asymmetries in Wald statistics. The findings are country-specific and it would be regionally specific.

Weibull Parameter Estimation — A Comparison of Different Methods

2005 ◽  
Vol 29 (3) ◽  
pp. 309-315 ◽  
Author(s):  
H Basumatary ◽  
E Sreevalsan ◽  
K K Sasi
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Standard Deviation ◽  
Wind Speed ◽  
Probability Function ◽  
Time Series Data ◽  
Series Data ◽  
Weibull Parameter ◽  
Wind Regime ◽  
Wind Regimes

The Weibull probability function is a widely accepted tool to model wind regimes. This paper presents a comparative study of different methods used to estimate Weibull parameters of a wind regime. Five different methods are described and used for the estimation. Time series data of wind speed over a whole year for two sites have been used for the study. The results obtained as a plot of error versus wind speed are similar in all the five methods, yet the method of standard deviation gives the best results.

scholarly journals Autocovariance and autocorrelation structures of the generalised autoregressive moving average (GARMA(1,3;δ,1)) model

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Suguneswary Ellappan ◽  
Norhashidah Awang ◽  
Thulasyammal Ramiah Pillai
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arma Model ◽  
Series Data ◽  
Autoregressive Moving Average ◽  
Numerical Examples ◽  
New Class ◽  
Autocorrelation Structure ◽  
Special Case

Generalized ARMA (GARMA) model is a new class of model that has been introduced to reveal some unknown features of certain time series data. The objective of this paper is to derive the autocovariance and autocorrelation structure of GARMA(1,3;δ,1)  model in order to study the behaviour of the model. It is shown that the results of this model can be reduced to the autocovariance and autocorrelation of the standard ARMA model as well as a special case. Numerical examples are used to illustrate the behaviour of the autocovariance and autocorrelation at different δ values to show the various structures that the model can represent

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