scholarly journals Development of copper price from July 1959 and predicted development till the end of year 2022

2021 ◽  
pp. 262-280
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
High Performance ◽  
Short Term Memory ◽  
Copper Price ◽  
Future Price ◽  
Copper Ore ◽  
Experimental Prediction ◽  
One Year

The increasingly meagre copper ore resources constitute one of the decisive factors influencing the price of this commodity. The demand for copper has been showing an accelerating trend since the Covid pandemic broke out. It is thereby imperative to estimate the future price movement of this material. The article focuses on a daily prediction of the forthcoming change in prices of copper on the commodity market. The research data were gathered from day-to-day closing historical prices of copper from commodity stock COMEX converted to a time series. The price is expressed in US Dollars per pound. The data were processed using artificial intelligence, recurrent neural networks, including the Long Short Term Memory layer. Neural networks have a great potential to predict this type of time series. The results show that the volatility in copper price during the monitored period was low or close to zero. We may thereby argue that neural networks foresee the first three months more accurately than the rest of the examined period. Neural structures anticipate copper prices from 4.5 to 4.6 USD to the end of the period in question. Low volatility that would last longer than one year would cut down speculators’ profits to a minimum (lower risk). On the other hand, this situation would bring about balance which the purchasing companies avidly seek for. However, the presented article is solely confined to a limited number of variables to work with, disregarding other decisive criteria. Although the very high performance of the experimental prediction model, there is always space for improvement – e.g. effectively combining traditional methods with advanced techniques of artificial intelligence.

scholarly journals Minutely Active Power Forecasting Models Using Neural Networks

2020 ◽  
Vol 12 (8) ◽  
pp. 3177 ◽  
Author(s):  
Dimitrios Kontogiannis ◽  
Dimitrios Bargiotas ◽  
Aspassia Daskalopulu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Power Systems ◽  
Demand Response ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Absolute Error ◽  
Real World Data ◽  
Training Time ◽  
Power Forecasting

Power forecasting is an integral part of the Demand Response design philosophy for power systems, enabling utility companies to understand the electricity consumption patterns of their customers and adjust price signals accordingly, in order to handle load demand more effectively. Since there is an increasing interest in real-time automation and more flexible Demand Response programs that monitor changes in the residential load profiles and reflect them according to changes in energy pricing schemes, high granularity time series forecasting is at the forefront of energy and artificial intelligence research, aimed at developing machine learning models that can produce accurate time series predictions. In this study we compared the baseline performance and structure of different types of neural networks on residential energy data by formulating a suitable supervised learning problem, based on real world data. After training and testing long short-term memory (LSTM) network variants, a convolutional neural network (CNN), and a multi-layer perceptron (MLP), we observed that the latter performed better on the given problem, yielding the lowest mean absolute error and achieving the fastest training time.

scholarly journals Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

2018 ◽  
Vol 10 (3) ◽  
pp. 452 ◽  
Author(s):  
Yun-Long Kong ◽  
Qingqing Huang ◽  
Chengyi Wang ◽  
Jingbo Chen ◽  
Jiansheng Chen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Short Term Memory ◽  
Satellite Image ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Disturbance Detection

scholarly journals Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1942
Author(s):  
Pyae Pyae Phyo ◽  
Yung-Cheol Byun
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Absolute Error ◽  
Percentage Error ◽  
Ensemble Model ◽  
Load Prediction ◽  
Energy Prediction ◽  
The Times ◽  
One Year

The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.

Downtime Technique Using Artificial Intelligence: A Case Study for an Exposed Berthing Facility

2019 ◽  
Author(s):  
Ghassan El Chahal ◽  
Peter A. B. Morel ◽  
Sindhu Mole ◽  
Nadjib Saadali
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Environmental Conditions ◽  
Wind Wave ◽  
Wind Waves ◽  
Large Set ◽  
Mike 21 ◽  
Environmental Time Series

Abstract Hydrodynamic modelling is significantly improved in the last decade, however, the coupling of these hydrodynamic models with methods to estimate berth downtime due to environmental conditions (wind, waves, currents) is less developed. The large number of environmental inputs (wind speed, wind direction, wave height, wave period, wave direction, current speed, current direction) and mooring outputs (vessel motions in six degrees of freedom, mooring lines, fender forces) to be considered in a downtime study requires major simplifications as the full wind-wave time series cannot be calculated. Downtime assessment is normally simplified by calculating a limited number of wind-wave combinations. A new approach is developed and presented in this paper by calculating the downtime for the long-term environmental time series using artificial neural networks. Neural networks are some of the most capable artificial intelligence tools for solving very complex problems like the present case. The approach has been applied for a bulk terminal project located in the Arabian Sea. The terminal has no shelter and is totally exposed to wind and swell waves. The wave climate for a 15 years period is established at the project site using spectral wave modelling software MIKE 21 SW. A large set of combined environmental conditions is selected for the dynamic mooring analysis of the vessel at berth. The time domain mooring analysis software MIKE 21 MA is used in this study. An inhouse Matlab code/program is developed using neural network to calculate the downtime for the long-term environmental time series based on the dynamic vessel response for the set of selected environmental combinations. This approach provides a more accurate downtime estimate which is important for the operability of such exposed facilities. The downtime tool is also tested for a different set of environmental combinations and mooring layouts in order to assess the sensitivity of these parameters on the downtime estimate. Up to the authors’ knowledge, this is the first published work applying artificial intelligence techniques for downtime studies.

Deep machine learning for detection of acoustic wave reflections

2019 ◽  
Vol 19 (5) ◽  
pp. 1340-1350
Author(s):  
Mulugeta A Haile ◽  
Edward Zhu ◽  
Christopher Hsu ◽  
Natasha Bradley
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Acoustic Emission ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Propagating Waves ◽  
Long Short Term Memory

Acoustic emission signals are information rich and can be used to estimate the size and location of damage in structures. However, many existing algorithms may be deceived by indirectly propagated acoustic emission waves which are modulated by reflection boundaries within the structures. We propose two deep learning models to identify such waves such that existing algorithms for damage detection and localization may be used. The first approach uses long short-term memory recurrent neural networks to learn distinct patterns directly from the time-series data. In the second approach, we transform the time-series data into spectrograms and utilize convolutional neural networks to perform binary classification by leveraging spectro-temporal features. We achieved 80% classification accuracy using long short-term memory and near-perfect accuracy using convolutional neural networks on a dataset of acoustic emission signals generated by the Hsu-Nielsen sources. Both long short-term memory and convolutional neural network models were able to learn general and context-specific features of the direct and reflected acoustic emission waves. Once accurately identified, the indirectly propagating waves are filtered out while the directly propagating waves are used for source location using existing methods.

scholarly journals Estimation of the development of the Euro to Chinese Yuan exchange rate using artificial neural networks

2019 ◽  
Vol 61 ◽  
pp. 01030 ◽  
Author(s):  
Marek Vochozka ◽  
Jaromír Vrbka
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Exchange Rate ◽  
Financial Institutions ◽  
Current Status ◽  
Medium Term ◽  
Future Developments ◽  
Long Run ◽  
Chinese Yuan

The exchange rate is one of the most monitored economic variables, from the position of individual citizens or economists, financial institutions or entrepreneurs. In the long run, it is a reflection of the condition of the economy, and in the short and medium term it has a significant impact on the economy. The time series of currency development maps past developments, current status, and is also able to predict future developments. This article analyzes the time series of the development of EUR to Yuan exchange rate using artificial intelligence. It aims to evaluate this development and to indicate the prediction of the future development of EUR to Yuan.

scholarly journals Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 149 ◽  
Author(s):  
Salah Bouktif ◽  
Ali Fiaz ◽  
Ali Ouni ◽  
Mohamed Adel Serhani
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Time Lags ◽  
Electric Load ◽  
Learning Approaches ◽  
Electric Load Forecasting

Time series analysis using long short term memory (LSTM) deep learning is a very attractive strategy to achieve accurate electric load forecasting. Although it outperforms most machine learning approaches, the LSTM forecasting model still reveals a lack of validity because it neglects several characteristics of the electric load exhibited by time series. In this work, we propose a load-forecasting model based on enhanced-LSTM that explicitly considers the periodicity characteristic of the electric load by using multiple sequences of inputs time lags. An autoregressive model is developed together with an autocorrelation function (ACF) to regress consumption and identify the most relevant time lags to feed the multi-sequence LSTM. Two variations of deep neural networks, LSTM and gated recurrent unit (GRU) are developed for both single and multi-sequence time-lagged features. These models are compared to each other and to a spectrum of data mining benchmark techniques including artificial neural networks (ANN), boosting, and bagging ensemble trees. France Metropolitan’s electricity consumption data is used to train and validate our models. The obtained results show that GRU- and LSTM-based deep learning model with multi-sequence time lags achieve higher performance than other alternatives including the single-sequence LSTM. It is demonstrated that the new models can capture critical characteristics of complex time series (i.e., periodicity) by encompassing past information from multiple timescale sequences. These models subsequently achieve predictions that are more accurate.

Empirical Inference of Numerical Information into Causal Strategy Models by Means of Artificial Intelligence

2011 ◽  
pp. 290-313
Author(s):  
Christian Hillbrand
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Time Series ◽  
Forecasting Model ◽  
Empirical Validation ◽  
Functional Dependencies ◽  
Cause And Effect ◽  
Decision Supporting ◽  
Approximate Simulation

The motivation for this chapter is the observation that many companies build their strategy upon poorly validated hypotheses about cause and effect of certain business variables. However, the soundness of these cause-and-effect-relations as well as the knowledge of the approximate shape of the functional dependencies underlying these associations turns out to be the biggest issue for the quality of the results of decision supporting procedures. Since it is sufficiently clear that mere correlation of time series is not suitable to prove the causality of two business concepts, there seems to be a rather dogmatic perception of the inadmissibility of empirical validation mechanisms for causal models within the field of strategic management as well as management science. However, one can find proven causality techniques in other sciences like econometrics, mechanics, neuroscience, or philosophy. Therefore this chapter presents an approach which applies a combination of well-established statistical causal proofing methods to strategy models in order to validate them. These validated causal strategy models are then used as the basis for approximating the functional form of causal dependencies by the means of Artificial Neural Networks. This in turn can be employed to build an approximate simulation or forecasting model of the strategic system.

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