Multiple Time-scale Features Research Articles

A Distributed Scheme for the Taxi Cruising Route Recommendation Problem Using a Graph Neural Network

Despite considerable research efforts being devoted to the taxi cruising route recommendation (TCRR) problem, existing studies still have some shortcomings. To begin with, the competition and collaboration between taxis are not sufficiently taken into account. Furthermore, the TCRR is heavily reliant on potential taxi demand, which is time-variant and difficult to accurately predict due to the underlying spatiotemporal correlation and dynamic traffic patterns. Moreover, the consideration of competition and cooperation among taxis increases the complexity of the TCRR problem, making conventional centralized algorithms computationally expensive. In this paper, we first formulate TCRR as a biobjective optimization problem to balance the collaboration and competition between taxis. Subsequently, we forecast short-term taxi demand using the proposed long-short-term-memory-based graph convolutional network (LSTM-GCN), which considers diverse factors such as road topology, points of interest (POIs), and multiple time-scale features. Lastly, we propose a distributed algorithm based on a Lagrange dual decomposition. The experimental and simulation results demonstrate that our TCRR scheme performs better than any other counterpart, (i) resulting in a 3% reduction in idle taxis per hour, (ii) performing four times faster than the centralized algorithms to obtain the optimal solution, and (iii) resulting in a 7% increase in average profit.

Autism detection based on multiple time scale model

Objective. Current autism clinical detection relies on doctor observation and filling of clinical scales, which is subjective and prone to misdetection. Existing autism research of functional magnetic resonance imaging (fMRI) over-compresses the time-scale information and has poor generalization ability. This study extracts multiple time scale brain features of fMRI, providing objective detection. Approach. We first use least absolute shrinkage and selection operator to build a sparse network and extract features with a time scale of 1. Then, we use hidden markov model to extract features that describe the dynamic changes of the brain, with a time scale of 2. Additionally, to analyze the features of the potential network activity of autism from a higher time scale, we use long short-term memory to construct an auto-encoder to re-encode the original data and extract the features at a higher time scale, with a time scale of T, and T is the time length of fMRI. We use recursive feature elimination for feature selection for three different time scale features, merge them into multiple time scale features, and finally use one-dimensional convolution neural network for classification. Main results. Compared with well-established models, our method has achieved better results. The accuracy of our method is 76.0%, and the area under the roc curve is 0.83, tested on completely independent data, so our method has better generalization ability. Significance. This research analyzes fMRI sequences from multiple time scale to detect autism, and it also provides a new framework and research ideas for subsequent fMRI analysis.

Deep learning with multiple scale attention and direction regularization for asset price prediction

Forecasting the stock price is a challenging task due to its complex dynamic behaviors, affected by long-term trends, seasonal changes, cyclical changes, and irregular changes. Although many deep learning techniques have been applied to stock price forecasting, few of them have a deep insight into these complex behaviors. In this work, we propose a four-step hybrid model, named ESTA-Net, to adaptively extract these behavior patterns for stock price forecasting. Firstly, the empirical mode decomposition is applied to decompose a closing price sequence into intrinsic mode functions (IMFs). The goal of this step is to extract multiple quasi-stationary features of different time scales from the historical closing price sequence. Secondly, each IMF is modeled and forecasted by a temporal attention long short term memory (TALSTM) network. The TALSTM network is designed to capture the long term dependency of each IMF. Thirdly, the learned deep representations of IMFs are fed into a scale attention network (SANet), which adaptively selects relevant deep representations of multiple time scale features extracted from the historical price sequence. Finally, these learned deep features are fed into a fully connected layer to predict the future closing price. In addition, to make the proposed model learn the movement direction of the closing price, we propose a novel regularization term, i.e. the direction regularization term, to train the proposed model. This regularization term measures the inconsistency between the predicted movement direction and the actual movement direction of the closing price. Experiments show that the proposed model significantly outperforms benchmark models. Particularly, on seven financial market indices, the proposed model with the direction regularization term achieves the highest POCID (32.04% higher than that of CNN) and the lowest MAPE (37.36% lower than that of DA-RNN).

Predicting the trend of stock index based on feature engineering and CatBoost model

As an important tool to measure the current situation of the whole stock market, the stock index has always been the focus of researchers, especially for its prediction. This paper uses trend types, which are received by clustering price series under multiple time scale, combined with the day-of-the-week effect to construct a categorical feature combination. Based on the historical data of six kinds of Chinese stock indexes, the CatBoost model is used for training and predicting. Experimental results show that the out-of-sample prediction accuracy is 0.55, and the long–short trading strategy can obtain average annualized return of 34.43%, which is a great improvement compared with other classical classification algorithms. Under the rolling back-testing, the model can always obtain stable returns in each period of time from 2012 to 2020. Among them, the SSESC’s long–short strategy has the best performance with an annualized return of 40.85% and a sharp ratio of 1.53. Therefore, the trend information on multiple time-scale features based on feature engineering can be learned by the CatBoost model well, which has a guiding effect on predicting stock index trends.

Predicting the Trend of Stock Market Index Using the Hybrid Neural Network Based on Multiple Time Scale Feature Learning

In the stock market, predicting the trend of price series is one of the most widely investigated and challenging problems for investors and researchers. There are multiple time scale features in financial time series due to different durations of impact factors and traders’ trading behaviors. In this paper, we propose a novel end-to-end hybrid neural network, a model based on multiple time scale feature learning to predict the price trend of the stock market index. Firstly, the hybrid neural network extracts two types of features on different time scales through the first and second layers of the convolutional neural network (CNN), together with the raw daily price series, reflect relatively short-, medium- and long-term features in the price sequence. Secondly, considering time dependencies existing in the three kinds of features, the proposed hybrid neural network leverages three long short-term memory (LSTM) recurrent neural networks to capture such dependencies, respectively. Finally, fully connected layers are used to learn joint representations for predicting the price trend. The proposed hybrid neural network demonstrates its effectiveness by outperforming benchmark models on the real dataset.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations.

Quantitative Assessment on Multiple Timescale Features and Dynamics of Sea Surface Suspended Sediment Concentration Using Remote Sensing Data

AbstractWind‐waves, tidal currents, and some other dynamic factors dominate the suspended sediment concentration (SSC) variations in shallow seas and it is difficult to quantitatively evaluate the effects of individual dynamic factors on SSC modulation. This work used the long‐term Moderate Resolution Imaging Spectroradiometer (MODIS) and the high temporal‐resolution Geostationary Ocean Color Imager (GOCI) remote sensing data to quantify the sea surface SSC variations on multiple timescales (intratidal, spring‐neap, seasonal, and long‐term timescales) in the Bohai Sea, and further quantitatively evaluated the effects of corresponding dynamic factors on the SSC modulation. The results indicated that the monsoon associated wind‐waves and stratification played the most important role in modulating SSC, with seasonal SSC variation of 8.1 mg/L in the Bohai Sea. The intratidal current variations played the secondary important role, causing SSC variation of 5.8 mg/L. The spring‐neap tidal current variations led to SSC variation of 3.1 mg/L in the Bohai Sea. In the long run (2003–2014), the SSC of the Bohai Sea decreased slightly with SSC variation of 2.8 mg/L (decline rate: 0.23 mg/L/year), which may be caused by the weakening wind, decreasing sediment load from the Yellow River or the massive reclamation in recent decades. Probably due to the topography, sea bed sediment grain size and river plume, SSC variations in the southern Bohai Sea were more pronounced than those in the northern Bohai Sea.

Integrated Finite-Time Disturbance Observer and Controller Design for Reusable Launch Vehicle in Reentry Phase

AbstractAn integrated finite-time disturbance observer (FDO) and attitude controller is designed for a reusable launch vehicle (RLV) in this paper. In accordance with the multiple-timescale features, RLV attitude dynamics are divided into an outer-loop subsystem and an inner-loop subsystem. Based on the recently developed sliding mode control (SMC), a novel multivariable supertwisting sliding mode controller driven by a FDO is designed to achieve a fast and accurate reentry attitude tracking. This integrated design can generate a continuous control law which has excellent robustness to uncertainty and disturbances with known bounds while achieving an arbitrarily fast convergence. The finite-time stability of the overall system is proved by using the Lyapunov function technique and the multiple-timescale separation principle. In addition, an optimal control allocation for allocating torque commands into aerodynamic surface deflection commands with constraints is also proposed. Finally, the effectiveness an...