Time Series Data Cleaning: A Survey <s> D. GENERATIVE ADVERSARIAL NETWORKS <s> It's getting more difficult to find available parking spaces with the rapid growth of the vehicles, especially in big cities. The parking guidance system (PGS) which powered by real time data or historical data could reduce the time spent on looking for parking spaces and alleviate the heavy traffic around the parking lots. However, the PGS is completely ineffective when the parking lots missing data or even have no parking data. This paper makes an exploration on data repairing by digging geospatial data and historical data. First, this paper proposes a method to verify the possibility of parking data similarity when parking lots have spatial similarity. Then take the known data of parking lots as samples and generate reparative data through the Recurrent GANs. The experiment shows, when parking lots have similar spatial features, the parking data, in a high probability, have some similarity with each other, too. And the data generated by Recurrent GANs have the same distribution with real data. In fact, this paper provides a new idea to solve the problems of timeseries data repairing, to a certain extent, this method can help to save the cost of equipment and time. <s> BIB001 </s> Time Series Data Cleaning: A Survey <s> D. GENERATIVE ADVERSARIAL NETWORKS <s> The high costs and pollutant emissions of vehicles have raised the demand for reducing fuel consumption globally. The idea is to improve the operations of vehicles without losing the output power such that the engine speed and torque work with the minimum fuel consumption rate. It relies on the complete map of engine speed and torque to fuel consumption rate, known as the engine universal characteristic map. Unfortunately, such a map is often incomplete (fuel consumption rate not observed under most engine speed and torque combinations) and inconsistent (different fuel consumption rates observed under the same engine speed and torque combination). In this paper, we propose to predict the fine-grained fuel consumption rate of each engine speed and torque combination, by learning a model from the incomplete and inconsistent observation data. A novel FuelNet is designed based on Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). Deconvolution is employed to predict the incomplete fuel consumption rates, while the discriminator can successfully tolerate the inconsistent fuel consumption rate observations. Experiments show that our FuelNet outperforms the existing approaches in both imputing the incomplete and repairing the inconsistent fuel consumption rates. Remarkably, we deploy the predicted fine-grained fuel consumption rates in a mobile application to assist driving, and show that the fuel consumption can be reduced up to 12.8%. <s> BIB002
With the rapid development of machine learning technology, more and more problems are solved using machine learning. Li et al.  use the GANs network to effectively detect anomalies in time series data. GANs trains two models at the same time, which are the generation model for capturing data distribution and the discriminant model for discriminating whether the data are real data or pseudo data as shown in Figure 8 . Given a random variable with a probability of uniform distribution as input, we want to generate a probability distribution of the output as ''dog probability distribution''. The philosophy of Generative Matching Networks (GMNs), which idea is to train the generative network by directly comparing the generated distribution with the true distribution, is to optimize the network by repeating the following steps: (1) Generate some evenly distributed input; (2) Let these inputs go through the network and collect the generated output; (3) Compare the true ''dog probability distribution'' with the generated ''dog probability distribution'' based on the available samples (e.g. calculate the MMD distance between the real dog image sample and the generated image sample); (4) Use backpropagation and gradient descent to compute the errors and update the weights. The purpose of this process is to minimize the loss of the generation model and discriminant. Li et al.  use GANs to detect abnormalities in time series and a natural idea is to use GANs network to repair missing values of time series data. Perhaps more machine learning algorithms are waiting for the cleaning of time series error values. A simple idea is to treat the detected anomaly data as missing data and then repair it. Sun et al. BIB001 first analyze the similarity between parking space data and parking data, and then use Recurrent GANs to generate parking data as repair data, which provide a new idea for solving the problem of time series data repair. Fang et al. BIB002 propose FuelNet which is based on Convolutional Neural Networks (CNNs) and GANs. FuelNet is used to repair the inconsistent and impute the incomplete fuel consumption rates over time.