Early detection of rising talents is of paramount importance in the field of advertising. In this paper, we define a concept of talent breakout and propose a method to detect Japanese talents before their rise to stardom. The main focus of the study is to determine the effectiveness of combining Twitter and TV data on predicting time-dependent changes in social data. Although traditional time-series models are known to be robust in many applications, the success of neural network models in various fields (e.g.\ Natural Language Processing, Computer Vision, Reinforcement Learning) continues to spark an interest in the time-series community to apply new techniques in practice. Therefore, in order to find the best modeling approach, we have experimented with traditional, neural network and ensemble learning methods. We observe that ensemble learning methods outperform traditional and neural network models based on standard regression metrics. However, by utilizing the concept of talent breakout, we are able to assess the true forecasting ability of the models, where neural networks outperform traditional and ensemble learning methods in terms of precision and recall.

In the simulations in subsection 3.2, all linear networks have The backbone architecture consists of a stack of 12 Conv-LSTM modules, and each module contains 32 units (channels). The backbone architecture is illustrated in Figure 7. To demonstrate ARMA networks' applicability to image segmentation, we evaluate it on a challenging The network architecture is illustrated in Figure 15a. The experimental results are summarized in Table 5. Since image classifications tasks do not require convolu-tional layers to have large receptive fields, the learned autoregressive coefficients concentrate around 0, as shown in Figure 6.

The objective of the paper is to price weather derivative contracts based on temperature and precipitation as underlying climate variables.

The paper considers the problem to estimate non-causal graphical models whose edges encode smoothing relations among the variables. We propose a new covariance extension problem and show that the solution minimizing the transportation distance with respect to white noise process is a double-sided autoregressive non-causal graphical model. Then, we generalize the paradigm to a class of graphical autoregressive moving-average models. Finally, we test the performance of the proposed method through some numerical experiments.

We view the ARMA (Autoregressive Moving Average) model of a stationary process as a random variable approximation. By mapping stationary random variables to Hardy space functions on the unit disk, we can turn a problem of random variable approximation to a newly formulated problem of function approximation. When the functions are continuous, the spectral theorem provides a link between these two points of view, allowing us to provide approximation guarantees for a certain class of stationary processes, and also to identify certain other stationary processes that seem difficult to approximate. We were unable to find similar approximation or approximability guarantees in our review of time series and distributed lags literature. For instance, as detailed in the next section, Box and Jenkins ([BJ76]) assume that a long moving average representation obtained from Wold's decomposition can be mapped to a short autoregressive representation based on some examples and analogies, and provide no specific guarantees for general stationary processes. They tackle the existence of a stable ARMA model using the notion of unit roots.

In the field of transportation, it is of paramount importance to address and mitigate illegal actions committed by both motor and non-motor vehicles. Among those actions, wrong-way cycling (i.e., riding a bicycle or e-bike in the opposite direction of the designated traffic flow) poses significant risks to both cyclists and other road users. To this end, this paper formulates a problem of detecting wrong-way cycling ratios in CCTV videos. Specifically, we propose a sparse sampling method called WWC-Predictor to efficiently solve this problem, addressing the inefficiencies of direct tracking methods. Our approach leverages both detection-based information, which utilizes the information from bounding boxes, and orientation-based information, which provides insights into the image itself, to enhance instantaneous information capture capability. On our proposed benchmark dataset consisting of 35 minutes of video sequences and minute-level annotation, our method achieves an average error rate of a mere 1.475% while taking only 19.12% GPU time of straightforward tracking methods under the same detection model. This remarkable performance demonstrates the effectiveness of our approach in identifying and predicting instances of wrong-way cycling.

We study the quadratic prediction error method -- i.e., nonlinear least squares -- for a class of time-varying parametric predictor models satisfying a certain identifiability condition. While this method is known to asymptotically achieve the optimal rate for a wide range of problems, there have been no non-asymptotic results matching these optimal rates outside of a select few, typically linear, model classes. By leveraging modern tools from learning with dependent data, we provide the first rate-optimal non-asymptotic analysis of this method for our more general setting of nonlinearly parametrized model classes. Moreover, we show that our results can be applied to a particular class of identifiable AutoRegressive Moving Average (ARMA) models, resulting in the first optimal non-asymptotic rates for identification of ARMA models.

We develop a new efficient sequential approximate leverage score algorithm, SALSA, using methods from randomized numerical linear algebra (RandNLA) for large matrices. We demonstrate that, with high probability, the accuracy of SALSA's approximations is within $(1 + O({\varepsilon}))$ of the true leverage scores. In addition, we show that the theoretical computational complexity and numerical accuracy of SALSA surpass existing approximations. These theoretical results are subsequently utilized to develop an efficient algorithm, named LSARMA, for fitting an appropriate ARMA model to large-scale time series data. Our proposed algorithm is, with high probability, guaranteed to find the maximum likelihood estimates of the parameters for the true underlying ARMA model. Furthermore, it has a worst-case running time that significantly improves those of the state-of-the-art alternatives in big data regimes. Empirical results on large-scale data strongly support these theoretical results and underscore the efficacy of our new approach.

ANY modern digital platforms involve the acquisition of data over networks, while network data has a stationary process models, ARMA models widely used in typically time-varying structure. For instance, measurements classical signal processing have also been adapted to graph acquired on a sensor network or user data in a social network domains in several recent works [5], [6]. Meanwhile, the often vary over time. Such data can be modeled as timevarying computation of an ARMA process model is a challenging graph signals, or time-vertex signals. In many practical problem in graph domains as it typically involves the solution applications, time-vertex signals may have missing observations of highly nonlinear and nonconvex optimization problems. The due to issues such as sensor failure, connection loss, and problem of learning graph ARMA process models has been partial availability of user statistics. Hence, the spatio-temporal addressed in the previous studies [3], [5], [6]; however, none of interpolation of time-vertex signals arises as an important these studies explicitly aim to capture the specific time-vertex problem of interest.