scholarly journals Machine Learning of Time Series Using Time-Delay Embedding and Precision Annealing

2019 ◽  
Vol 31 (10) ◽  
pp. 2004-2024 ◽  
Author(s):  
Alexander J. A. Ty ◽  
Zheng Fang ◽  
Rivver A. Gonzalez ◽  
Paul. J. Rozdeba ◽  
Henry D. I. Abarbanel
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Delay ◽  
Global Minimum ◽  
Statistical Data ◽  
Dimensional Space ◽  
Network Models ◽  
Multilayer Perceptrons ◽  
Task Time ◽  
Scalar Time Series

Tasking machine learning to predict segments of a time series requires estimating the parameters of a ML model with input/output pairs from the time series. We borrow two techniques used in statistical data assimilation in order to accomplish this task: time-delay embedding to prepare our input data and precision annealing as a training method. The precision annealing approach identifies the global minimum of the action ([Formula: see text]). In this way, we are able to identify the number of training pairs required to produce good generalizations (predictions) for the time series. We proceed from a scalar time series [Formula: see text] and, using methods of nonlinear time series analysis, show how to produce a [Formula: see text]-dimensional time-delay embedding space in which the time series has no false neighbors as does the observed [Formula: see text] time series. In that [Formula: see text]-dimensional space, we explore the use of feedforward multilayer perceptrons as network models operating on [Formula: see text]-dimensional input and producing [Formula: see text]-dimensional outputs.

Empirical analysis of time series using feature selection algorithms

2021 ◽  
Author(s):  
Mikhail Kanevski
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Feature Selection ◽  
Dimensional Space ◽  
Multivariate Time Series ◽  
Feature Space ◽  
Real Data ◽  
Theoretical Concepts ◽  
Wide Range ◽  
Linear And Nonlinear

<p>Nowadays a wide range of methods and tools to study and forecast time series is available. An important problem in forecasting concerns embedding of time series, i.e. construction of a high dimensional space where forecasting problem is considered as a regression task. There are several basic linear and nonlinear approaches of constructing such space by defining an optimal delay vector using different theoretical concepts. Another way is to consider this space as an input feature space – IFS, and to apply machine learning feature selection (FS) algorithms to optimize IFS according to the problem under study (analysis, modelling or forecasting). Such approach is an empirical one: it is based on data and depends on the FS algorithms applied. In machine learning features are generally classified as relevant, redundant and irrelevant. It gives a reach possibility to perform advanced multivariate time series exploration and development of interpretable predictive models.</p><p>Therefore, in the present research different FS algorithms are used to analyze fundamental properties of time series from empirical point of view. Linear and nonlinear simulated time series are studied in detail to understand the advantages and drawbacks of the proposed approach. Real data case studies deal with air pollution and wind speed times series. Preliminary results are quite promising and more research is in progress.</p>

scholarly journals A Brief Survey of Telerobotic Time Delay Mitigation

2020 ◽  
Vol 7 ◽  
Author(s):  
Parinaz Farajiparvar ◽  
Hao Ying ◽  
Abhilash Pandya
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Network Models ◽  
Future Research ◽  
Generative Adversarial Network ◽  
User Intent ◽  
Adversarial Network ◽  
Delay Prediction

There is a substantial number of telerobotics and teleoperation applications ranging from space operations, ground/aerial robotics, drive-by-wire systems to medical interventions. Major obstacles for such applications include latency, channel corruptions, and bandwidth which limit teleoperation efficacy. This survey reviews the time delay problem in teleoperation systems. We briefly review different solutions from early approaches which consist of control-theory-based models and user interface designs and focus on newer approaches developed since 2014. Future solutions to the time delay problem will likely be hybrid solutions which include modeling of user intent, prediction of robot movements, and time delay prediction all potentially using time series prediction methods. Hence, we examine methods that are primarily based on time series prediction. Recent prediction approaches take advantage of advances in nonlinear statistical models as well as machine learning and neural network techniques. We review Recurrent Neural Networks, Long Short-Term Memory, Sequence to Sequence, and Generative Adversarial Network models and examine each of these approaches for addressing time delay. As time delay is still an unsolved problem, we suggest some possible future research directions from information-theory-based modeling, which may lead to promising new approaches to advancing the field.

STATISTICAL AND MACHINE LEARNING HIGH-FREQUENCY TIME SERIES FORECASTING METHODS IN AUTOMATIC MODE

2020 ◽  
pp. 3-11
Author(s):  
Sai Van Cuong ◽  
M. V. Shcherbakov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
High Frequency ◽  
Network Models ◽  
Time Series Forecasting ◽  
Neural Network Models ◽  
Automatic Mode ◽  
Forecasting Methods ◽  
The Neural Network

The research of the problem of automatic high-frequency time series forecasting (without expert) is devoted. The efficiency of high-frequency time series forecasting using different statistical and machine learning modelsis investigated. Theclassical statistical forecasting methods are compared with neural network models based on 1000 synthetic sets of high-frequency data. The neural network models give better prediction results, however, it takes more time to compute compared to statistical approaches.

scholarly journals A Simple Dendritic Neural Network Model-Based Approach for Daily PM2.5 Concentration Prediction

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 373
Author(s):  
Zhenyu Song ◽  
Cheng Tang ◽  
Junkai Ji ◽  
Yuki Todo ◽  
Zheng Tang
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Time Delay ◽  
Real World ◽  
Search Algorithm ◽  
Dimensional Space ◽  
Neural Model ◽  
Concentration Time ◽  
Concentration Time Series ◽  
Pm2.5 Concentration

Air pollution in cities has a massive impact on human health, and an increase in fine particulate matter (PM2.5) concentrations is the main reason for air pollution. Due to the chaotic and intrinsic complexities of PM2.5 concentration time series, it is difficult to utilize traditional approaches to extract useful information from these data. Therefore, a neural model with a dendritic mechanism trained via the states of matter search algorithm (SDNN) is employed to conduct daily PM2.5 concentration forecasting. Primarily, the time delay and embedding dimensions are calculated via the mutual information-based method and false nearest neighbours approach to train the data, respectively. Then, the phase space reconstruction is performed to map the PM2.5 concentration time series into a high-dimensional space based on the obtained time delay and embedding dimensions. Finally, the SDNN is employed to forecast the PM2.5 concentration. The effectiveness of this approach is verified through extensive experimental evaluations, which collect six real-world datasets from recent years. To the best of our knowledge, this study is the first attempt to utilize a dendritic neural model to perform real-world air quality forecasting. The extensive experimental results demonstrate that the SDNN offers very competitive performance relative to the latest prediction techniques.

Using Machine Learning to Assess Yield Impacts of Crop Rotation: Combining Satellite and Statistical Data for Ukraine

2020 ◽  
Author(s):  
Klaus Deininger ◽  
Daniel Ayalew Ali ◽  
Nataliia Kussul ◽  
Mykola Lavreniuk ◽  
Oleg Nivievskyi
Keyword(s):  
Machine Learning ◽  
Crop Rotation ◽  
Statistical Data

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

Sign in / Sign up

Export Citation Format

Share Document