A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

A hybrid fuzzy quantum time series and linear programming model: Special application on TAIEX index dataset

2019 ◽  
Vol 34 (25) ◽  
pp. 1950201 ◽  
Author(s):  
Pritpal Singh ◽  
Gaurav Dhiman ◽  
Sen Guo ◽  
Ritika Maini ◽  
Harsimran Kaur ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Programming ◽  
Real Time ◽  
Time Series Data ◽  
Programming Model ◽  
Series Data ◽  
Quantum Model ◽  
Quantum Time ◽  
Degree Of Membership ◽  
Quantum Approach

The supremacy of quantum approach is able to provide the solutions which are not practically feasible on classical machines. This paper introduces a novel quantum model for time series data which depends on the appropriate length of intervals. In this study, the effects of these drawbacks are elaborately illustrated, and some significant measures to remove them are suggested, such as use of degree of membership along with mid-value of the interval. All these improvements signify the effective results in case of quantum time series, which are verified and validated with real-time datasets.

A near real-time water surface detection method based on HSV transformation of MODIS multi-spectral time series data

2014 ◽  
Vol 140 ◽  
pp. 704-716 ◽  
Author(s):  
J.-F. Pekel ◽  
C. Vancutsem ◽  
L. Bastin ◽  
M. Clerici ◽  
E. Vanbogaert ◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Water Surface ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data ◽  
Surface Detection

scholarly journals Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach

2022 ◽  
Vol 3 (1) ◽  
pp. 1-26
Author(s):  
Omid Hajihassani ◽  
Omid Ardakanian ◽  
Hamzeh Khazaei
Keyword(s):  
Time Series ◽  
Linear Transformation ◽  
Input Data ◽  
Time Series Data ◽  
Representation Learning ◽  
Sensor Data ◽  
Series Data ◽  
Sensitive Information ◽  
Latent Space ◽  
Low Dimensional

The abundance of data collected by sensors in Internet of Things devices and the success of deep neural networks in uncovering hidden patterns in time series data have led to mounting privacy concerns. This is because private and sensitive information can be potentially learned from sensor data by applications that have access to this data. In this article, we aim to examine the tradeoff between utility and privacy loss by learning low-dimensional representations that are useful for data obfuscation. We propose deterministic and probabilistic transformations in the latent space of a variational autoencoder to synthesize time series data such that intrusive inferences are prevented while desired inferences can still be made with sufficient accuracy. In the deterministic case, we use a linear transformation to move the representation of input data in the latent space such that the reconstructed data is likely to have the same public attribute but a different private attribute than the original input data. In the probabilistic case, we apply the linear transformation to the latent representation of input data with some probability. We compare our technique with autoencoder-based anonymization techniques and additionally show that it can anonymize data in real time on resource-constrained edge devices.

scholarly journals Matrix Profile-Based Approach to Industrial Sensor Data Analysis Inside RDBMS

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2146
Author(s):  
Mikhail Zymbler ◽  
Elena Ivanova
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Wide Spectrum ◽  
Database Management System ◽  
Third Party ◽  
Sensor Data ◽  
Data Conversion ◽  
Series Data ◽  
Time Interval ◽  
Short Time Interval

Currently, big sensor data arise in a wide spectrum of Industry 4.0, Internet of Things, and Smart City applications. In such subject domains, sensors tend to have a high frequency and produce massive time series in a relatively short time interval. The data collected from the sensors are subject to mining in order to make strategic decisions. In the article, we consider the problem of choosing a Time Series Database Management System (TSDBMS) to provide efficient storing and mining of big sensor data. We overview InfluxDB, OpenTSDB, and TimescaleDB, which are among the most popular state-of-the-art TSDBMSs, and represent different categories of such systems, namely native, add-ons over NoSQL systems, and add-ons over relational DBMSs (RDBMSs), respectively. Our overview shows that, at present, TSDBMSs offer a modest built-in toolset to mine big sensor data. This leads to the use of third-party mining systems and unwanted overhead costs due to exporting data outside a TSDBMS, data conversion, and so on. We propose an approach to managing and mining sensor data inside RDBMSs that exploits the Matrix Profile concept. A Matrix Profile is a data structure that annotates a time series through the index of and the distance to the nearest neighbor of each subsequence of the time series and serves as a basis to discover motifs, anomalies, and other time-series data mining primitives. This approach is implemented as a PostgreSQL extension that allows an application programmer both to compute matrix profiles and mining primitives and to represent them as relational tables. Experimental case studies show that our approach surpasses the above-mentioned out-of-TSDBMS competitors in terms of performance since it assumes that sensor data are mined inside a TSDBMS at no significant overhead costs.

Sign in / Sign up

Export Citation Format

Share Document