365,400 research outputs found
Recurrent Neural Networks Applied to GNSS Time Series for Denoising and Prediction
Global Navigation Satellite Systems (GNSS) are systems that continuously acquire data and provide position time series. Many monitoring applications are based on GNSS data and their efficiency depends on the capability in the time series analysis to characterize the signal content and/or to predict incoming coordinates. In this work we propose a suitable Network Architecture, based on Long Short Term Memory Recurrent Neural Networks, to solve two main tasks in GNSS time series analysis: denoising and prediction. We carry out an analysis on a synthetic time series, then we inspect two real different case studies and evaluate the results. We develop a non-deep network that removes almost the 50% of scattering from real GNSS time series and achieves a coordinate prediction with 1.1 millimeters of Mean Squared Error
A neural network for mining large volumes of time series data
Efficiently mining large volumes of time series data is amongst the most challenging problems that are fundamental in many fields such as industrial process monitoring, medical data analysis and business forecasting. This paper discusses a high-performance neural network for mining large time series data set and some practical issues on time series data mining. Examples of how this technology is used to search the engine data within a major UK eScience Grid project (DAME) for supporting the maintenance of Rolls-Royce aero-engine are presented
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Echo state network for occupancy prediction and pattern mining in intelligent environment
Pattern analysis and prediction of sensory data is becoming an increasing scientific challenge and a massive economical interest supports the need for better pattern mining techniques. The aim of this paper is to investigate efficient mining of useful information from a sensor network representing an ambient intelligence environment. The goal is to extract and predict behavioral patterns of a person in his/her daily activities by analyzing the time series data representing the behaviour of the occupant, generated using occupancy sensors. There are various techniques available for analysis and prediction of a continuous time series signal. However, the occupancy signal is represented by a binary time series where only discrete values of a signal are available. To build the prediction model, recurrent neural networks are investigated. They are proven to be useful tools to solve the difficulties of the temporal relationships of inputs between observations at different time steps, by maintaining internal states that have memory. In this paper, a special form of recurrent neural network, the so-called Echo State Network (ESN) is used in which discrete values of time series can be well processed. Then, a model developed based on ESN is compared with the most popular recurrent neural net-works; namely Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL). The results showed that ESN provides better prediction results compared with BPTT and RTRL. Using ESN, large datasets are learnt in only few minutes or even seconds. It can be concluded that ESN are efficient and valuable tools in binary time series prediction. The results presented in this paper are based on simulated data generated from a simulator representing a person in a 1 bedroom flat
Searching for Exoplanets Using Artificial Intelligence
In the last decade, over a million stars were monitored to detect transiting
planets. Manual interpretation of potential exoplanet candidates is labor
intensive and subject to human error, the results of which are difficult to
quantify. Here we present a new method of detecting exoplanet candidates in
large planetary search projects which, unlike current methods uses a neural
network. Neural networks, also called "deep learning" or "deep nets" are
designed to give a computer perception into a specific problem by training it
to recognize patterns. Unlike past transit detection algorithms deep nets learn
to recognize planet features instead of relying on hand-coded metrics that
humans perceive as the most representative. Our convolutional neural network is
capable of detecting Earth-like exoplanets in noisy time-series data with a
greater accuracy than a least-squares method. Deep nets are highly
generalizable allowing data to be evaluated from different time series after
interpolation without compromising performance. As validated by our deep net
analysis of Kepler light curves, we detect periodic transits consistent with
the true period without any model fitting. Our study indicates that machine
learning will facilitate the characterization of exoplanets in future analysis
of large astronomy data sets.Comment: Accepted, 16 Pages, 14 Figures,
https://github.com/pearsonkyle/Exoplanet-Artificial-Intelligenc
Forecasting of Weekly Electricity Consumption Using Neural Networks
Neural networks have been shown to be effective in modelling time series, with applications in the forecasting of electricity consumption. In applying neural networks to weekly electricity consumption data, several issues, such as selection of network architecture, network structure and input structure need to be addressed. This paper addresses these issues in relation to the current application and also demonstrates that considerable value is to be gained from incorporating the lessons learned from linear time series modelling into the current nonlinear analysis. Results for national Irish weekly electricity data demonstrate the potential improvements which can be obtained using the neural network approach
Forecasting Saving Deposit in Malaysian Islamic Banking: Comparison Between Artificial Neural Network and Arima
The aim of this paper is to test the ability of artificial neural network (ANN) as an alternative method in time series forecasting and compared to autoregresÂsive integrated moving average (ARIMA) in studying saving deposit in MalayÂsian Islamic banks. Artificial neural network is getting popular as an alternaÂtive method in time series forecasting for its capability to capture volaÂtility pattern of non-linear time series data. In addition, the use of an estabÂlished tool of analysis such as ARIMA is of importance here for comparative purposes. These two methods are applied to monthly data of the Malaysian Islamic bankÂing deposits from January 1994 to November 2005. The result provides evidence that ANN using “early stopping” approach can be used as an alternaÂtive forecasting engine with univariate time series model. It can predict non-linÂear time series using the pattern of the data directly without any statistiÂcal analysis
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