Performance Of Time Series Prediction Research Articles

Ship Trajectory Prediction Based on the TTCN-Attention-GRU Model

As shipping continues to play an increasingly important role in world trade, there are consequently a large number of ships at sea at any given time, posing a risk to maritime traffic safety. Therefore, the tracking and monitoring of ships at sea has gradually attracted the attention of scholars. Ship trajectory prediction comprises an important aspect of ship tracking and monitoring. Trajectory prediction describes the forecasting of a ship’s future trajectory over a period of time through use of historical trajectory information of the ship, so as to predict the sailing dynamics of the ship in advance. Accurate trajectory prediction can help maritime regulatory authorities improve supervision efficiency and reduce collisions between ships. Temporal Convolutional Network (TCN) offers good time memory ability and has shown better performance in time series prediction in recent years. Ship trajectory sequence belongs to the category of time series. Thus, in this paper, we introduce TCN into the field of ship trajectory prediction and improve on it, and propose Tiered-TCN (TTCN). The attention mechanism is a way to help neural networks learn data features by highlighting features that have a greater impact on predicted values. Gate Recurrent Unit (GRU) is an important variant of Recurrent Neural Networks (RNN), which bears a strong nonlinear fitting ability. In this paper, TTCN, attention mechanism and GRU network are integrated to construct a hybrid model for trajectory prediction, which is referred to as TTCN-Attention-GRU (TTAG). By optimizing the advantages of each module, the prediction effect is achieved with high precision. The experimental results show that the TTAG model is superior to all the baseline models presented in this paper.

Decision-level machinery fault prognosis using N-BEATS-based degradation feature prediction and reconstruction

Condition monitoring signals provide sufficient information about the health of machines and, therefore, are widely used for fault diagnosis, prognosis, and health management. Existing approaches generally extract one or more degradation features from original signals collected in a time interval and predict the remaining useful life of machinery based on a selected or fused feature under a pre-specified threshold. However, using a single feature is often inadequate in terms of the accuracy of fault prognosis due to the interval-based extraction procedure. To overcome the shortcoming, a new prognosis framework is proposed for machinery based on original condition monitoring signals in this paper. Technically, the Box-Cox transformation is first performed on the original signals point by point to construct a series of degradation features without losing information. Then, the neural basis expansion analysis for time series (N-BEATS) that has robust performance in time-series prediction is utilized to predict the future evolution for each feature with high volatility. By leveraging the similarity of multiple Box-Cox features, a parameter-based transfer learning method is proposed to reduce the computation complexity. Finally, we reconstruct the future original signals of machinery through the inverse Box-Cox transformation. Since the reconstructed original signals are noisy, a new failure criterion suitable for decision-level fault prognosis is defined from an industrial application perspective. An application on high speed train wheels and two follow-up simulations are used to illustrate the performance of our proposed framework in machinery fault prognosis.

A novel method of prediction for capacity and remaining useful life of lithium-ion battery based on multi-time scale Weibull accelerated failure time regression

Lithium-ion batteries are essential energy storage components for electrical grid, and the health diagnosis determines the safety of the battery during usage and the rational classify of echelon utilization. In this article, a multi-timescale capacity and lifespan prediction method is proposed where capacity prediction and remaining useful life prediction are divided into the short-time scale and the long-time scale. For capacity prediction, the long short term memory neural network with five significant features is applied according to its accuracy performance in time series prediction. As for remaining useful life, the Weibull accelerated failure time regression is proposed to improve the prediction efficiency of a large amount of data. Finally, the predictive capability, robustness and effectiveness of proposed methods are verified using two datasets with different cycling test conditions within an error of 3.9 % in long-time scale and 2.7 % in short-time scale. The proposed method has great potential for targeted and accurate health state forecasting and long-term end of life prediction.

Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting.

The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine-cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.

A Prediction Method with Data Leakage Suppression for Time Series

In view of the characteristics of the collected time series, such as being high noise, non-stationary and nonlinear, most of the current methods are designed to smooth or denoise the whole time series at one time and then divide the training set and testing set, which will lead to using the information of the testing set in the training process, resulting in data leakage and other problems. In order to reduce the impact of noise on time series prediction and prevent data leakage, a prediction method with data leakage suppression for time series (DLS) is proposed. This prediction method carries out multiple variational mode decomposition on the time series by overlapping slicing and improves the noise reduction threshold function to perform noise reduction processing on the decomposed time series. Furthermore, the idea of deep learning is introduced to establish a neural network multi-step prediction model, so as to improve the performance of time series prediction. The different datasets are selected as experimental data, and the results show that the proposed method has a better prediction effect and lower prediction error, compared with the common multi-step prediction methods, which verifies the superiority of the prediction method.

A self-adaptive grey forecasting model and its application

GM(1,1) models have been widely used in various fields due to their high performance in time series prediction. However, some hypotheses of the existing GM(1, 1) model family may reduce their prediction performance in some cases. To solve this problem, this paper proposes a self-adaptive GM(1, 1) model, termed as SAGM(1, 1) model, which aims to solve the defects of the existing GM (1,1) model family by deleting their modeling hypothesis. Moreover, a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed, the proposed multi-parameter optimization scheme adopts machine learning ideas, takes all adjustable parameters of SAGM(1, 1) model as input variables, and trains it with firefly algorithm. And Sobol' sensitivity indices are applied to study global sensitivity of SAGM(1, 1) model parameters, which provides an important reference for model parameter calibration. Finally, forecasting capability of SAGM(1, 1) model is illustrated by Anhui electricity consumption dataset. Results show that prediction accuracy of SAGM(1, 1) model is significantly better than other models, and it is shown that the proposed approach enhances the prediction performance of GM(1,1) model significantly.

Maximum Power Demand Prediction Using Fbprophet With Adaptive Kalman Filtering

It is very difficult to predict the Maximum Power Demand (MPD) of customers in high performance because of various factors. In this paper, the problem of MPD prediction is studied by using fused machine learning algorithms. Firstly, an improved grey relation analysis method is adopted to analyze relevant influencing factors. Secondly, a modified prediction algorithm based on an adaptive cubature Kalman filter combined with Fbprophet is proposed according to the characteristics of customers' MPD. Finally, the proposed algorithm of this paper is applied to predict MPD and cost is evaluated. Experiment results show that the improved MPD prediction algorithm can comprehensively consider the relevant factors, and has good performance in time series prediction.

Analysis of Asia Pacific stock markets with a novel multiscale model

Stock price prediction is considered a challenging task in the field of financial time series prediction. In recent years, the application of new data mining techniques, including empirical mode decomposition (EMD), to financial time series prediction has attracted increasing attention. Unfortunately, EMD has two major shortcomings when applied to this task: (1) EMD has been traditionally applied to very long time series, and is subject to a long incubation period precluding its real-time application. (2) After the application of EMD, large volumes of data are produced, and some form of dimensionality reduction is still required. In order to solve these problems and improve EMD’s performance in time series prediction, this paper proposes a hybrid model combining EMD, principal component analysis (PCA) and BP neural network (BPNN). This novel hybrid model is based on concepts of decomposition and information fusion. In order to evaluate its forecasting performance, the proposed model was compared with other four typical models, with prediction metrics demonstrating its superiority, including in terms of directional symmetry (DS).

BBS Posts Time Series Analysis based on Sample Entropy and Deep Neural Networks.

The modeling and forecasting of BBS (Bulletin Board System) posts time series is crucial for government agencies, corporations and website operators to monitor public opinion. Accurate prediction of the number of BBS posts will assist government agencies or corporations in making timely decisions and estimating the future number of BBS posts will help website operators to allocate resources to deal with the possible hot events pressure. By combining sample entropy (SampEn) and deep neural networks (DNN), an approach (SampEn-DNN) is proposed for BBS posts time series modeling and forecasting. The main idea of SampEn-DNN is to utilize SampEn to decide the input vectors of DNN with smallest complexity, and DNN to enhance the prediction performance of time series. Selecting Tianya Zatan new posts as the data source, the performances of SampEn-DNN were compared with auto-regressive integrated moving average (ARIMA), seasonal ARIMA, polynomial regression, neural networks, etc. approaches for prediction of the daily number of new posts. From the experimental results, it can be found that the proposed approach SampEn-DNN outperforms the state-of-the-art approaches for BBS posts time series modeling and forecasting.

The improved grey model based on particle swarm optimization algorithm for time series prediction

Grey theory is one of the most common methods for solving uncertain problems using limited data and poor information, due to its high performance in time series prediction. However, the inappropriate background value and initial value are the main factors affecting prediction accuracy of the Grey Model GM(1,1). An improved grey model based on particle swarm optimization algorithm named PGM(1,1) is proposed for time series prediction in this paper. The development coefficient of the grey model is calculated by PGM(1,1) based on particle swarm optimization, targeting at minimizing the average relative errors between the restored value and real value of the model to avoid the problem caused by background value optimization. In addition, the initial value of the Grey Model GM(1,1) is optimized and a sliding window is introduced to improve both precision and adaptability. Finally, three data sets, featuring increasing trend, decreasing trend, and wide fluctuations, are used in the experiments, showing that the proposed method achieves better prediction accuracy.

Fading memory and time series prediction in recurrent networks with different forms of plasticity

We investigate how different forms of plasticity shape the dynamics and computational properties of simple recurrent spiking neural networks. In particular, we study the effect of combining two forms of neuronal plasticity: spike timing dependent plasticity (STDP), which changes the synaptic strength, and intrinsic plasticity (IP), which changes the excitability of individual neurons to maintain homeostasis of their activity. We find that the interaction of these forms of plasticity gives rise to interesting network dynamics characterized by a comparatively large number of stable limit cycles. We study the response of such networks to external input and find that they exhibit a fading memory of recent inputs. We then demonstrate that the combination of STDP and IP shapes the network structure and dynamics in ways that allow the discovery of patterns in input time series and lead to good performance in time series prediction. Our results underscore the importance of studying the interaction of different forms of plasticity on network behavior.

Fast sequential implementation of "neural-gas" network for vector quantization

Although the network proposed by Martinetz et al. in 1993 has been proven for its optimality in vector quantizer design and has been demonstrated to have good performance in time-series prediction, its high computational complexity (NlogN) makes it a slow sequential algorithm. We suggest two ideas to speedup its sequential realization: (1) using a truncated exponential function as its neighborhood function and (2) applying a new extension of the partial distance elimination method (PDE). This fast realization is compared with the original version of the neural-gas network for codebook design in image vector quantization. The comparison indicates that a speedup of five times is possible, while the quality of the resulting codebook is almost the same as that of the straightforward realization.