scholarly journals A Deep Learning Based Method for Forecasting Gold Price with Respect to Pandemics

2020 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Alireza Abbasi
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Specific Model ◽  
Gold Price ◽  
Series Data ◽  
Trade Relations ◽  
Market Values ◽  
The World ◽  
Proper Estimation ◽  
Validation Set

Abstract The spread of COVID-19 in the world had a devastating impact on the world economy, trade relations, and globalization. As the pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in a similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to the COVID-19 dataset using long short-term memory networks (LSTM).The nature of the features in each pandemic is totally different, thus, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognizing and extracting the features which affect the pandemic is the highest priority. In this study, we develop a framework, providing a better understanding of the features and feature selection. This study is based on a preliminary analysis of such features for enhancing forecasting models' performance against fluctuations in the market.Our forecasts are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, Vector Output Sequence LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM on our dataset, and our results achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for one day, two days, and 30 days predictions in advance which are outperforming other proposed method in the literature.

A Deep Learning Based Methods for Forecasting Gold Price with Respect to Pandemics

2020 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Alireza Abbasi
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Specific Model ◽  
Gold Price ◽  
Series Data ◽  
Trade Relations ◽  
Market Values ◽  
The World ◽  
Proper Estimation ◽  
Validation Set

Abstract The spread of COVID-19 in the world had a devastating impact on the world economy, trade relations, and globalization. As the pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to COVID-19 dataset using long short-term memory networks (LSTM).The nature of the features in each pandemic is totally different, thus, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognising and extracting the features which affect the pandemic is in the highest priorities. In this study, we develop a framework, providing a better understanding of the features and feature selection. This study is based on a preliminary analysis of such features for enhancing forecasting models' performance against fluctuations in the market.Our forecasts are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, Vector Out-put Sequence LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM on our dataset and our results achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for one day, two days, and 30 days predictions in advance which is outperforming other proposed method in the literature.

scholarly journals A Deep Learning Based Method for Forecasting Gold Price with Respect to Pandemics

2021 ◽  
Author(s):  
Mahtab Mohtasham Khani ◽  
Sahand Vahidnia ◽  
Alireza Abbasi
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Specific Model ◽  
Gold Price ◽  
Series Data ◽  
Trade Relations ◽  
Market Values ◽  
The World ◽  
Global Impacts ◽  
Proper Estimation

Abstract The spread of COVID-19 in the world had a devastating impact on the world economy, trade relations, and globalization. As the pandemic advances and new potential pandemics are on the horizon, a precise analysis of recent fluctuations of trade becomes necessary for international decisions and controlling the world in a similar crisis. The COVID-19 pandemic made a new pattern of trade in the world and affected how businesses work and trade with each other. It means that every potential pandemic or any unprecedented event in the world can change the market rules. This research develops a novel model to have a proper estimation of the stock market values with respect to the COVID-19 dataset using long short-term memory networks (LSTM).The nature of the features in each pandemic is completely different, thus, prediction results for a pandemic by a specific model cannot be applied to other pandemics. Hence, recognizing and extracting the features which affect the pandemic is the highest priority. In this study, we develop a framework, providing a better understanding of the features and feature selection. Although the global impacts incurred by COVID-19 are complicated, here we are trying to show how additional features like COVID-19 rather than the history of tickers, which is used conventionally for prediction, cab help forecasting in a real-world scenario. This study is based on a preliminary analysis of such features for enhancing forecasting models' performance against fluctuations in the market.Our forecasts are based on the market value data and COVID-19 pandemic daily time-series data (i.e. the number of new cases). In this study, we selected Gold price as a base for our forecasting task which can be replaced by any other markets. We have applied Convolutional Neural Networks (CNN) LSTM, Vector Output Sequence LSTM, Bidirectional LSTM, and Encoder-Decoder LSTM on our dataset, and our results achieved an MSE of 6.0e-4, 8.0e-4, and 2.0e-3 on the validation set respectfully for one day, two days, and 30 days predictions in advance which are outperforming other proposed method in the literature.

scholarly journals The Influence of the World Gold Price, the Dow Jones Islamic Market, and the Composite Stock Price Index on the Jakarta Islamic Index for the 2014 - 2018 period

2021 ◽  
Vol 4 (1) ◽  
pp. 13
Author(s):  
Siti Chaerunisa Prastiani
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Price Index ◽  
Oil And Gas ◽  
Time Series Data ◽  
Research Output ◽  
Gold Price ◽  
Series Data ◽  
The World ◽  
Stock Price Index

This study aims to determine how much influence the variables of the World Gold Price and Stock Prices with proxies: Dow Jones Islamic Market (DJIM) stock prices, and the Composite Stock Price Index (IHSG), on the Jakarta Islamic Index (JII). This study uses a quantitative approach, namely data that is measured in a numerical scale, based on the 2014-2018 Time Series data relating to variables sourced from the Central Statistics Agency, the Indonesia Stock Exchange and the Directorate General of Oil and Gas. This research uses one of the SPSS Series. The variables in this study consist of World Gold Price (X1), Dow Jones Islamic Market (DJIM) (X2), Composite Stock Price Index (IHSG) (X3) against the Jakarta Islamic Index (JII) (Y). The purpose of this research is to know each variable partially or simultaneously from the variable World Gold Price, Dow Jones Islamic Market and the Jakarta Islamic Index. Research Output expected by an Accredited journal

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

Prediction and Analysis of Gold Prices using Ensemble Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 4367-4374
Author(s):  
Gudipally Chandrashakar
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Gold Price ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Average Value ◽  
Ensemble Machine Learning

In this article, we used historical time series data up to the current day gold price. In this study of predicting gold price, we consider few correlating factors like silver price, copper price, standard, and poor’s 500 value, dollar-rupee exchange rate, Dow Jones Industrial Average Value. Considering the prices of every correlating factor and gold price data where dates ranging from 2008 January to 2021 February. Few algorithms of machine learning are used to analyze the time-series data are Random Forest Regression, Support Vector Regressor, Linear Regressor, ExtraTrees Regressor and Gradient boosting Regression. While seeing the results the Extra Tree Regressor algorithm gives the predicted value of gold prices more accurately.

scholarly journals FAKTOR – FAKTOR YANG MEMPENGARUHI KONSUMSI GAS DI INDONESIA

2021 ◽  
Vol 3 (2) ◽  
pp. 69
Author(s):  
Rohim Rohim ◽  
Mike Triani
Keyword(s):  
Time Series ◽  
World Bank ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Industrial Growth ◽  
A Value ◽  
The World Bank ◽  
The World ◽  
Gas Consumption

The purpose of this research is to determine (1) the effect of income on gas consumption in Indonesia (2) the effect of population on gas consumption in Indonesia (3) the effect of industrial growth on gas consumption in Indonesia. This type of research is descriptive and associative. The data used in this research is secondary data from Indonesia in the form of time series data from 1970 to 2019 and this data was obtained from official institutions of the World Bank and BP Statistic World. The data were processed using multiple linear regression. The results showed that the income had a negative and significant effect on gas consumption with a probability value of 0.0005 <0.05, the population had a positive and significant effect on gas consumption with a value of prob t-count of 0.0010 <0.05 and industrial growth had a positive and significant effect on gas consumption.  The significant to gas consumption in Indonesia with a value of prob t-count value of 0.5219 <0.05 and suggestions for further researchers to be able to analyze other factors that affecting gas consumption in Indonesia.  Because from the gas sectors, there are still many factors that affected gas consumption until the research results will be better

scholarly journals VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS

2019 ◽  
Vol 17 (12.2) ◽  
pp. 61
Author(s):  
Nguyen Ngoc Tra ◽  
Ho Phuoc Tien ◽  
Nguyen Thanh Dat ◽  
Nguyen Ngoc Vu
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Future Trend ◽  
Series Data ◽  
Short Term ◽  
Trend Prediction ◽  
Temporal Dependence ◽  
Term Memory ◽  
Long Short Term Memory

The paper attemps to forecast the future trend of Vietnam index (VN-index) by using long-short term memory (LSTM) networks. In particular, an LSTM-based neural network is employed to study the temporal dependence in time-series data of past and present VN index values. Empirical forecasting results show that LSTM-based stock trend prediction offers an accuracy of about 60% which outperforms moving-average-based prediction.

scholarly journals Tourism Demand Forecasting Based on an LSTM Network and Its Variants

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 243
Author(s):  
Shun-Chieh Hsieh
Keyword(s):  
Adaptive Learning ◽  
Time Series Data ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Series Data ◽  
Learning Approaches ◽  
Tourism Demand ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Gated Recurrent Unit

The need for accurate tourism demand forecasting is widely recognized. The unreliability of traditional methods makes tourism demand forecasting still challenging. Using deep learning approaches, this study aims to adapt Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit networks (GRU), which are straightforward and efficient, to improve Taiwan’s tourism demand forecasting. The networks are able to seize the dependence of visitor arrival time series data. The Adam optimization algorithm with adaptive learning rate is used to optimize the basic setup of the models. The results show that the proposed models outperform previous studies undertaken during the Severe Acute Respiratory Syndrome (SARS) events of 2002–2003. This article also examines the effects of the current COVID-19 outbreak to tourist arrivals to Taiwan. The results show that the use of the LSTM network and its variants can perform satisfactorily for tourism demand forecasting.

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