Recent years have witnessed the unprecedented rising of time series from almost all kindes of academic and industrial fields. Various types of deep neural network models have been introduced to time series analysis, but the important frequency information is yet lack of effective modeling. In light of this, in this paper we propose a wavelet-based neural network structure called multilevel Wavelet Decomposition Network (mWDN) for building frequency-aware deep learning models for time series analysis. mWDN preserves the advantage of multilevel discrete wavelet decomposition in frequency learning while enables the fine-tuning of all parameters under a deep neural network framework. Based on mWDN, we further propose two deep learning models called Residual Classification Flow (RCF) and multi-frequecy Long Short-Term Memory (mLSTM) for time series classification and forecasting, respectively. The two models take all or partial mWDN decomposed sub-series in different frequencies as input, and resort to the back propagation algorithm to learn all the parameters globally, which enables seamless embedding of wavelet-based frequency analysis into deep learning frameworks. Extensive experiments on 40 UCR datasets and a real-world user volume dataset demonstrate the excellent performance of our time series models based on mWDN. In particular, we propose an importance analysis method to mWDN based models, which successfully identifies those time-series elements and mWDN layers that are crucially important to time series analysis. This indeed indicates the interpretability advantage of mWDN, and can be viewed as an indepth exploration to interpretable deep learning.

Multilevel Wavelet Decomposition Network for Interpretable Time Series Analysis

If the input symbols to a communication channel represent the outcomes of a chance event on which bets are available at odds consistent with their probabilities (i.e., “fair” odds), a gambler can use the knowledge given him by the received symbols to cause his money to grow exponentially. The maximum exponential rate of growth of the gambler's capital is equal to the rate of transmission of information over the channel. This result is generalized to include the case of arbitrary odds.Thus we find a situation in which the transmission rate is significant even though no coding is contemplated. Previously this quantity was given significance only by a theorem of Shannon's which asserted that, with suitable encoding, binary digits could be transmitted over the channel at this rate with an arbitrarily small probability of error.

A New Interpretation of Information Rate

Road network is the core component of urban transportation, and it is widely useful in various traffic-related systems and applications. Due to its important role, it is essential to develop general, effective, and robust road network representation models. Although several efforts have been made in this direction, they cannot fully capture the complex characteristics of road networks. In this paper, we propose a novel Hierarchical Road Network Representation model, named HRNR, by constructing a three-level neural architecture, corresponding to "functional zone", "structural regions" and "road segments", respectively. To associate the three kinds of nodes, we introduce two matrices consisting of probability distributions for modeling segment-to-region assignment or region-to-zone assignment. Based on the two assignment matrices, we carefully devise two reconstruction tasks, either based on network structure or human moving patterns. In this way, our node presentations are able to capture both structural and functional characteristics. Finally, we design a three-level hierarchical update mechanism for learning the node embeddings through the entire network. Extensive experiment results on three real-world datasets for four tasks have shown the effectiveness of the proposed model.

Learning Effective Road Network Representation with Hierarchical Graph Neural Networks

Personalized Route Recommendation (PRR) aims to generate user-specific route suggestions in response to users' route queries. Early studies cast the PRR task as a pathfinding problem on graphs, and adopt adapted search algorithms by integrating heuristic strategies. Although these methods are effective to some extent, they require setting the cost functions with heuristics. In addition, it is difficult to utilize useful context information in the search procedure. To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task. Our model consists of two components. First, we employ attention-based Recurrent Neural Networks (RNN) to model the cost from the source to the candidate location by incorporating useful context information. Instead of learning a single cost value, the RNN component is able to learn a time-varying vectorized representation for the moving state of a user. Second, we propose to use a value network for estimating the cost from a candidate location to the destination. For capturing structural characteristics, the value network is built on top of improved graph attention networks by incorporating the moving state of a user and other context information. The two components are integrated in a principled way for deriving a more accurate cost of a candidate location. Extensive experiment results on three real-world datasets have shown the effectiveness and robustness of the proposed model.

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

Since the 21st century, the global outbreaks of infectious diseases such as SARS in 2003, H1N1 in 2009, and H7N9 in 2013, have become the critical threat to the public health and a hunting nightmare to the government. Understanding the propagation in large-scale metapopulations and predicting the future outbreaks thus become crucially important for epidemic control and prevention. In the literature, there have been a bulk of studies on modeling intra-city epidemic propagation but with the single population assumption (homogeneity). Some recent works on metapopulation propagation, however, focus on finding specific human mobility physical networks to approximate diseases transmission networks, whose generality to fit different diseases cannot be guaranteed. In this paper, we argue that the intra-city epidemic propagation should be modeled on a metapopulation base, and propose a two-step method for this purpose. The first step is to understand the propagation system by inferring the underlying disease infection network. To this end, we propose a novel network inference model called D 2 PRI, which reduces the individual network into a sub-population network without information loss, and incorporates the power-law distribution prior and data prior for better performance. The second step is to predict the disease propagation by extending the classic SIR model to a metapopulation SIR model that allows visitors transmission between any two sub-populations. The validity of our model is testified on a real-life clinical report data set about the airborne disease in the Shenzhen city, China. The D 2 PRI model with the extended SIR model exhibit superior performance in various tasks including network inference, infection prediction and outbreaks simulation.

Inferring Metapopulation Propagation Network for Intra-city Epidemic Control and Prevention

Recent years have witnessed the continuous growth of megalopolises worldwide, which makes urban safety a top priority in modern city life. Among various threats, dangerous goods such as gas and hazardous chemicals transported through and around cities have increasingly become the deadly "bomb" we sleep with every day. In both academia and government, tremendous efforts have been dedicated to dealing with dangerous goods transportation (DGT) issues, but further study is still in great need to quantify the problem and explore its intrinsic dynamics in a big data perspective. In this paper, we present a novel system called DGeye, which features a "duet" between DGT trajectory data and human mobility data for risky zones identification. Moreover, DGeye innovatively takes risky patterns as the keystones in DGT management, and builds causality networks among them for pain point identification, attribution and prediction. Experiments on both Beijing and Tianjin cities demonstrate the effectiveness of DGeye. In particular, the report generated by DGeye driven the Beijing government to lay down gas pipelines for the famous Guijie food street.

No Longer Sleeping with a Bomb: A Duet System for Protecting Urban Safety from Dangerous Goods

How to build and improve forecasting models for stock prices are hot issues in both finance and computer fields in recent years Under the framework of reinforcement learning, the design of learning mechanism is important This paper presents a novel forecasting on trading Q-Iearning parameter adaptive forecasting framework (FTQL) Instead of learning from the forecasting errors directly, FTQL method proposes a new design of learning mechanism by considering the performance of trading strategy based on the forecasting results and asset allocation strategy FTQL firstly extracts the features from closing prices of both the underlying stock and its associated asset and set up the logistic regression (LR) models to provide directional forecasting Then, generate the investment signals according to the predictive results and apply an asset allocation strategy to execute the trading signals, for example, a variant of Uniform Constant Rebalanced Portfolios (UCRP) strategy Finally, Q-Iearning, the key point of FTQL, selects the best parameter combination of the signal generation models every day by taking the best actions according to the trained Q matrix FTQL proves its effectiveness and universality on parameter adaption in the volatile stock market

Forecasting on Trading: A Parameter Adaptive Framework Based on Q-Iearning

In recent years, ride-on-demand (RoD) services such as Uber and DiDi are becoming increasingly popular. Different from traditional taxi services, RoD services adopt dynamic pricing mechanisms to manipulate the supply and demand on the road, and such mechanisms improve service capacity and quality. Seeking route recommendation has been widely studied in taxi service. In RoD service, the dynamic price is a new and accurate indicator describing the supply and demand, but it is yet rarely studied in providing clues for drivers to seek for passengers. In this paper, we propose to incorporate the impacts of dynamic prices as a key factor in recommending seeking routes to drivers. We first justfiy why it is necessary to recommend seeking routes and consider dynamic prices, by analyzing real service data from a typical RoD service. We then design a reinforcement learning model based on order and GPS trajectories datasets, and take into account dynamic prices in the design. Results prove that our model improves both driver earnings and seeking strategies. On driver earnings, the reinforcement learning model increases revenue efficiency by up to 34.52%, and considering dynamic prices leads to another increase of 6.19%. On seeking strategies, drivers are encouraged to serve local demand first, and they are redistributed more evenly and effectively.

Seeking Based on Dynamic Prices: Higher Earnings and Better Strategies in Ride-on-Demand Services

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No Longer Sleeping with a Bomb: A Duet System for Protecting Urban Safety from Dangerous Goods

Forecasting on Trading: A Parameter Adaptive Framework Based on Q-Iearning

Seeking Based on Dynamic Prices: Higher Earnings and Better Strategies in Ride-on-Demand Services