scholarly journals Prediction Framework with Kalman Filter Algorithm

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 358
Author(s):  
Janis Peksa
Keyword(s):  
Kalman Filter ◽  
Time Series Data ◽  
Open Data ◽  
Series Data ◽  
Weather Station ◽  
Road Maintenance ◽  
Station Data ◽  
Weather Station Data ◽  
Data Prediction ◽  
Filter Approach

The article describes the autonomous open data prediction framework, which is in its infancy and is designed to automate predictions with a variety of data sources that are mostly external. The framework has been implemented with the Kalman filter approach, and an experiment with road maintenance weather station data is being performed. The framework was written in Python programming language; the frame is published on GitHub with all currently available results. The experiment is performed with 34 weather station data, which are time-series data, and the specific measurements that are predicted are dew points. The framework is published as a Web service to be able to integrate with ERP systems and be able to be reusable.

Nonstationary Kalman Filter for Estimation of Accurate and Consistent Car-Following Data

2005 ◽  
Vol 1934 (1) ◽  
pp. 2-12 ◽  
Author(s):  
Vincenzo Punzo ◽  
Domenico Josto Formisano ◽  
Vincenzo Torrieri
Keyword(s):  
Kalman Filter ◽  
Time Series Data ◽  
Data Consistency ◽  
Series Data ◽  
Trajectory Data ◽  
Car Following ◽  
Estimation Problems ◽  
Position Data ◽  
Data Collection Technique ◽  
Reference Measurements

Difficulty in obtaining accurate car-following data has traditionally been regarded as a considerable drawback in understanding real phenomena and has affected the development and validation of traffic microsimulation models. Recent advancements in digital technology have opened up new horizons in the conduct of research in this field. Despite the high degrees of precision of these techniques, estimation of time series data of speeds and accelerations from positions with the required accuracy is still a demanding task. The core of the problem is filtering the noisy trajectory data for each vehicle without altering platoon data consistency; i.e., the speeds and accelerations of following vehicles must be estimated so that the resulting intervehicle spacings are equal to the real one. Otherwise, negative spacings can also easily occur. The task was achieved in this study by considering vehicles of a platoon as a sole dynamic system and reducing several estimation problems to a single consistent one. This process was accomplished by means of a nonstationary Kalman filter that used measurements and time-varying error information from differential Global Positioning System devices. The Kalman filter was fruitfully applied here to estimation of the speed of the whole platoon by including intervehicle spacings as additional measurements (assumed to be reference measurements). The closed solution of an optimization problem that ensures strict observation of the true intervehicle spacings concludes the estimation process. The stationary counterpart of the devised filter is suitable for application to position data, regardless of the data collection technique used, e.g., video cameras.

scholarly journals Time-Series Classification Based on Fusion Features of Sequence and Visualization

2020 ◽  
Vol 10 (12) ◽  
pp. 4124
Author(s):  
Baoquan Wang ◽  
Tonghai Jiang ◽  
Xi Zhou ◽  
Bo Ma ◽  
Fan Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Human Brain ◽  
Time Series Data ◽  
Short Term Memory ◽  
Computational Cost ◽  
Open Data ◽  
Attention Mechanism ◽  
Series Data ◽  
Sequence Features ◽  
Fusion Features

For the task of time-series data classification (TSC), some methods directly classify raw time-series (TS) data. However, certain sequence features are not evident in the time domain and the human brain can extract visual features based on visualization to classify data. Therefore, some researchers have converted TS data to image data and used image processing methods for TSC. While human perceptionconsists of a combination of human senses from different aspects, existing methods only use sequence features or visualization features. Therefore, this paper proposes a framework for TSC based on fusion features (TSC-FF) of sequence features extracted from raw TS and visualization features extracted from Area Graphs converted from TS. Deep learning methods have been proven to be useful tools for automatically learning features from data; therefore, we use long short-term memory with an attention mechanism (LSTM-A) to learn sequence features and a convolutional neural network with an attention mechanism (CNN-A) for visualization features, in order to imitate the human brain. In addition, we use the simplest visualization method of Area Graph for visualization features extraction, avoiding loss of information and additional computational cost. This article aims to prove that using deep neural networks to learn features from different aspects and fusing them can replace complex, artificially constructed features, as well as remove the bias due to manually designed features, in order to avoid the limitations of domain knowledge. Experiments on several open data sets show that the framework achieves promising results, compared with other methods.

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