scholarly journals Machine learning forecasting of CR and PRC balance of trade

2020 ◽  
Vol 73 ◽  
pp. 01004
Author(s):  
Tomàš Brabenec ◽  
Petr Šuleř
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Data Set ◽  
Republic Of China ◽  
The People ◽  
The Future ◽  
Balance Of Trade ◽  
Political Economic ◽  
Social Importance

International trade is an important factor of economic growth. While foreign trade has existed throughout the history, its political, economic and social importance has grown significantly in the last centuries. The objective of the contribution is to use machine learning forecasting for predicting the balance of trade of the Czech Republic (CR) and the People´s Republic of China (PRC) through analysing and machine learning forecasting of the CR import from the PRC and the CR export to the PRC. The data set includes monthly trade balance intervals from January 2000 to June 2019. The contribution investigates and subsequently smooths two time series: the CR import from the PRC; the CR export to the PRC. The balance of trade of both countries in the entire monitored period is negative from the perspective of the CR. A total of 10,000 neural networks are generated. 5 neural structures with the best characteristics are retained. Neural networks are able to capture both the trend of the entire time series and its seasonal fluctuations, but it is necessary to work with time series lag. The CR import from the PRC is growing and it is expected to grow in the future. The CR export to the PRC is growing and it is expected to grow in the future, but its increase in absolute values will be slower than the increase of the CR import from the PRC.

scholarly journals Using Artificial Neural Networks for Equalizing Time Series Considering Seasonal Fluctuations

2019 ◽  
Vol 71 ◽  
pp. 01003
Author(s):  
J. Vrbka ◽  
J. Horák ◽  
V. Machová
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Seasonal Fluctuations ◽  
Republic Of China ◽  
The People ◽  
Data Points ◽  
Artificial Neural ◽  
The Individual

The objective of this contribution is to prepare a methodology of using artificial neural networks for equalizing time series when considering seasonal fluctuations on the example of the Czech Republic import from the People´s Republic of China. If we focus on the relation of neural networks and time series, it is possible to state that both the purpose of time series themselves and the nature of all the data are what matters. The purpose of neural networks is to record the process of time series and to forecast individual data points in the best possible way. From the discussion part it follows that adding other variables significantly improves the quality of the equalized time series. Not only the performance of the networks is very high, but the individual MLP networks are also able to capture the seasonal fluctuations in the development of the monitored variable, which is the CR import from the PRC.

Ship As a Wave Buoy: Estimating Relative Wave Direction From In-Service Ship Motion Measurements Using Machine Learning

2019 ◽  
Author(s):  
Bart Mak ◽  
Bülent Düz
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Machine Learning Algorithms ◽  
Ship Motion ◽  
Ship Motions ◽  
Wave Direction ◽  
Sea State ◽  
Time Series Regression ◽  
Data Set

Abstract For operations at sea it is important to have a good estimate of the current local sea state. Often, sea state information comes from wave buoys or weather forecasts. Sometimes wave radars are used. These sources are not always available or reliable. Being able to reliably use ship motions to estimate sea state characteristics reduces the dependency on external and/or expensive sources. In this paper, we present a method to estimate sea state characteristics from time series of 6-DOF ship motions using machine learning. The available data consists of ship motion and wave scanning radar measurements recorded for a period of two years on a frigate type vessel. The research focused on estimating the relative wave direction, since this is most difficult to estimate using traditional methods. Time series are well suited as input, since the phase differences between motion signals hold the information relevant for this case. This type of input data requires machine learning algorithms that can capture both the relation between the input channels and the time dependence. To this end, convolutional neural networks (CNN) and recurrent neural networks (RNN) are adopted in this study for multivariate time series regression. The results show that the estimation of the relative wave direction is acceptable, assuming that the data set is large enough and covers enough sea states. Investigating the chronological properties of the data set, it turned out that this is not yet the case. The paper will include discussions on how to interpret the results and how to treat temporal data in a more general sense.

scholarly journals Comparison of neural networks and regression time series in predicting export from Czech Republic into People´s Republic of China

2020 ◽  
Vol 73 ◽  
pp. 01015
Author(s):  
Tomáš Krulický ◽  
Tomáš Brabenec
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Czech Republic ◽  
Statistical Methods ◽  
Chinese Market ◽  
Republic Of China ◽  
The People ◽  
Short Period ◽  
The World ◽  
Year 2000

The People´s Republic of China is one of the largest, but also the most demanding markets in the world. The trade is limited by a number of barriers, strong competition and unusual environment for trades from other parts of the world. Despite those limitations, Czech exporters are able to establish themselves in the Chinese market, exporting mainly machines and vehicles. To predict future export trends is very difficult; however, these predictions can be crucial not only for individual exporters but also for the whole national economy. For predictions, economists use causal, intuitive or statistical methods. The objective of the contribution is to compare the accuracy of equalizing time series by means of regression analysis and artificial neural networks for a possible prediction of future export trends on the example of the Czech Republic export to the People´s Republic of China. For the purposes of analysis by means of statistical methods, the data obtained from monthly statements from the period starting from the year 2000 and ending in July 2018. First, a linear regression is carried out and subsequently, neural networks are used for regression. Finally, the results are compared. It appeared that in practice, mainly all retained neural networks are applicable. However, the first of them showed significant deviations within a very short period of time.

scholarly journals Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China

2021 ◽  
Vol 14 (2) ◽  
pp. 76
Author(s):  
Petr Suler ◽  
Zuzana Rowland ◽  
Tomas Krulicky
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Future Development ◽  
Explanatory Power ◽  
Republic Of China ◽  
China Trade ◽  
The Future ◽  
The Usa ◽  
Research Questions ◽  
Trade War

The objective of this contribution is to predict the development of the Czech Republic’s (CR) exports to the PRC (People’s Republic of China) using ANN (artificial neural networks). To meet the objective, two research questions are formulated. The questions focus on whether growth in the CR’s exports to the PRC can be expected and whether MLP (Multi-Layer Perceptron) networks are applicable for predicting the future development of the CR’s exports to the PRC. On the basis of previously obtained historical data, ANN with the best explanatory power are generated. For the purpose specified, three experiments are carried out, the results of which are described in detail. For the first, second and third experiments, ANN for predicting the development of exports are generated on the basis of a time series with a 1-month, 5-month and 10-month time delay, respectively. The generated ANN are the MLP and regression time series neural networks. The MLP turn out to be the most efficient in predicting the future development of the CR’s exports to the PRC. They are also able to predict possible extremes. It is also determined that the USA–China trade war has significantly affected the CR’s exports to the PRC.

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

scholarly journals Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

2021 ◽  
Author(s):  
Rogini Runghen ◽  
Daniel B Stouffer ◽  
Giulio Valentino Dalla Riva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Link Prediction ◽  
Graph Embedding ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Large Networks ◽  
Data Set ◽  
Learning Techniques ◽  
Low Dimensional

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

scholarly journals Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device

2021 ◽  
Vol 8 ◽  
Author(s):  
Tina Diao ◽  
Fareshta Kushzad ◽  
Megh D. Patel ◽  
Megha P. Bindiganavale ◽  
Munam Wasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Optic Neuropathy ◽  
Negative Response ◽  
Photopic Negative Response ◽  
Learning Approaches ◽  
Data Set ◽  
Full Field ◽  
Handheld Device ◽  
Technical Requirements

The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p < 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58–0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.

Combing empirical matched field processing at IMS station SPITS and convolutional neural networks for calving event detection in Svalbard

2021 ◽  
Author(s):  
Andreas Köhler ◽  
Steffen Mæland
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Networks ◽  
Event Detection ◽  
The Arctic ◽  
Beam Power ◽  
Seismic Events ◽  
Tidewater Glaciers ◽  
Seismic Arrays

<p>We combine the empirical matched field (EMF) method and machine learning using Convolutional Neural Networks (CNNs) for calving event detection at the IMS station SPITS and GSN station KBS on the Arctic Archipelago of Svalbard. EMF detection with seismic arrays seeks to identify all signals similar to a single template generated by seismic events in a confined target region. In contrast to master event cross-correlation detectors, the detection statistic is not the waveform similarity, but the array beam power obtained using empirical phase delays (steering parameters) between the array stations. Unlike common delay-and-sum beamforming, the steering parameters do not need to represent a plane wave and are directly computed from the template signal without assuming a particular apparent velocity and back-azimuth. As for all detectors, the false alarms rate depends strongly on the beam power threshold setting and therefore needs appropriate tuning or alternatively post-processing. Here, we combine the EMF detector using a low detection threshold with a post-detection classification step. The classifier uses spectrograms of single-station three-component records and state-of-the-art CNNs pre-trained for image recognition. Spectrograms of three-component seismic data are hereby combined as RGB images. We apply the methodology to detect calving events at tidewater glaciers in the Kongsfjord region in Northwestern Svalbard. The EMF detector uses data of the SPITS array, at about 100 km distance to the glaciers, while the CNN classifier processes data from the single three-component station KBS at 15 km distance using time windows where the event is expected according to the EMF detection. The EMF detector combines templates for the P and for the S wave onsets of a confirmed, large calving event. The CNN spectrogram classifier is trained using classes of confirmed calving signals from four different glaciers in the Kongsfjord region, seismic noise examples, and regional tectonic seismic events. By splitting the data into training and test data set, the CNN classifier yields a recognition rate of 89% on average. This is encouragingly high given the complex nature of calving signals and their visually similar waveforms. Subsequently, we process continuous data of 6 months in 2016 using the EMF-CNN method to produce a time series of glacier calving. About 90% of the confirmed calving signals used for the CNN training are detected by EMF processing, and around 80% are assigned to the correct glacier after CNN classification. Such calving time series allow us to estimate and monitor ice loss at tidewater glaciers which in turn can help to better understand the impact of climate change in Polar regions. Combining the superior detection capability of (less common) seismic arrays at a larger source distance with a powerful machine learning classifier at single three-component stations closer to the source, is a promising approach not only for environmental monitoring, but also for event detection and classification in a CTBTO verification context.</p>

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