Forecasting Gold Price in Rupiah using Multivariate Analysis with LSTM and GRU Neural Networks

Primananda, Sebastianus Bara; Isa, Sani Muhamad

doi:10.25046/aj060227

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LSTM model stores information on patterns in the data. LSTM can learn which data to keep and which data to discard, because each LSTM neuron has several gates, namely input gate (𝑖 𝑡 ), output gate (𝑜 𝑡 ) and forget gate (𝑓 𝑡 ) as well as the memory cell state value (∁ ́𝑡) in LSTM which regulates the memory of each neuron itself (Primananda & Isa, 2021). In the LSTM computation process, calculations are performed using the following formula:…”

Section: Long Short-term Memorymentioning

confidence: 99%

Gold Price Prediction Using the ARIMA and LSTM Models

Ferdinandus,

Kusrini,

Hidayat

2023

SinkrOn

View full text Add to dashboard Cite

For some investors who are interested in investing for the long term, gold is one of the promising options because the price of gold has recently continued to increase. In the current condition, gold investors generally use instinct and guesswork in investing in gold because there is a benchmark gold price based on world market prices. Many empirical studies identify factors that affect gold prices to forecast them. Factual and econometric analysis recommend different informative factors. This study investigates the influence of gold prices and five supporting variables in the form of economic indicators, namely crude oil price, federal funds effective rate, consumer price index, effective exchange rate and S&P 500 stock market index between 2002 and 2022. Models were built using ARIMA and LSTM methods, evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE). With a dataset allocation of 80% for training data and 20% for testing data, the comparison of actual gold prices with the predicted values of each model shows that LSTM has the best performance compared to the ARIMA (0,1,1) model where the LSTM model has an RMSE value of 8.124 and a MAPE value of 0.023. The models also show that economic indicators affect the ounce price of gold.

show abstract

Section: Long Short-term Memorymentioning

confidence: 99%

Gold Price Prediction Using the ARIMA and LSTM Models

Ferdinandus,

Kusrini,

Hidayat

2023

SinkrOn

View full text Add to dashboard Cite

show abstract

“…GRU a simplified variant of the LSTM network, comprises only two gate layers: the reset gate and the update gate. The reset gate determines the extent of previous memory information to discard [16]. Bidirectional-GRU (Bi-GRU) represents an advancement by merging the reverse RNN and GRU.…”

Section: Introductionmentioning

confidence: 99%

Application of Recurrent Neural Network Bi-Long Short-Term Memory, Gated Recurrent Unit and Bi-Gated Recurrent Unit for Forecasting Rupiah Against Dollar (USD) Exchange Rate

Fayyad,

Kurniawan,

Anugrah

et al. 2024

PREDATECS

View full text Add to dashboard Cite

Foreign exchange rates have a crucial role in a country's economic development, influencing long-term investment decisions. This research aims to forecast the exchange rate of Rupiah to the United States Dollar (USD) by using deep learning models of Recurrent Neural Network (RNN) architecture, especially Bi-Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-Gated Recurrent Unit (Bi-GRU). Historical daily exchange rate data from January 1, 2013 to November 3, 2023, obtained from Yahoo Finance, was used as the dataset. The model training and evaluation process was performed based on various parameters such as optimizer, batch size, and time step. The best model was identified by minimizing the Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Among the models tested, the GRU model with Nadam optimizer, batch size 16, and timestep 30 showed the best performance, with MSE 3741.6999, RMSE 61.1694, MAE 45.6246, and MAPE 0.3054%. The forecast results indicate a strengthening trend of the Rupiah exchange rate against the USD in the next 30 days, which has the potential to be taken into consideration in making investment decisions and shows promising economic growth prospects for Indonesia.

show abstract

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

Ayitey

Appiahene

Appiah

2022

Journal of Electrical Systems and Inf Technol

View full text Add to dashboard Cite

Since it is one of the world's most significant financial markets, the foreign exchange (Forex) market has attracted a large number of investors. Accurately anticipating the forex trend has remained a popular but difficult issue to aid Forex traders' trading decisions. It is always a question of how precise a Forex prediction can be because of the market's tremendous complexity. The fast advancement of machine learning in recent decades has allowed artificial neural networks to be effectively adapted to several areas, including the Forex market. As a result, a slew of research articles aimed at improving the accuracy of currency forecasting has been released. The Long Short-Term Memory (LSTM) neural network, which is a special kind of artificial neural network developed exclusively for time series data analysis, is frequently used. Due to its high learning capacity, the LSTM neural network is increasingly being utilized to predict advanced Forex trading based on previous data. This model, on the other hand, can be improved by stacking it. The goal of this study is to choose a dataset using the Hurst exponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural network to forecast the trend and conduct a correlation analysis. The Hurst exponent (h) was used to determine the predictability of the Australian Dollar and United States Dollar (AUD/USD) dataset. TLS-LSTM algorithm is presented to improve the accuracy of Forex trend prediction of Australian Dollar and United States Dollar (AUD/USD). A correlation study was performed between the AUD/USD, the Euro and the Australian Dollar (EUR/AUD), and the Australian Dollar and the Japanese Yen (AUD/JPY) to see how AUD/USD movement affects EUR/AUD and AUD/JPY. The model was compared with Single-Layer Long Short-Term (SL-LSTM), Multilayer Perceptron (MLP), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Improved Firefly Algorithm Long Short-Term Memory. Based on the evaluation metrics Mean Square Error (MSE), Root Mean Square Error, and Mean Absolute Error, the suggested TLS-LSTM, whose data selection is based on the Hurst exponent (h) value of 0.6026, outperforms SL-LSTM, MLP, and CEEMDAN-IFALSTM. The correlation analysis conducted shows both positive and negative relations between AUD/USD, EUR/AUD, and AUD/JPY which means that a change in AUD/USD will affect EUR/AUD and AUD/JPY as recorded depending on the magnitude of the correlation coefficient (r).

show abstract

Forecasting Gold Price in Rupiah using Multivariate Analysis with LSTM and GRU Neural Networks

Cited by 4 publications

References 16 publications

Gold Price Prediction Using the ARIMA and LSTM Models

Gold Price Prediction Using the ARIMA and LSTM Models

Application of Recurrent Neural Network Bi-Long Short-Term Memory, Gated Recurrent Unit and Bi-Gated Recurrent Unit for Forecasting Rupiah Against Dollar (USD) Exchange Rate

Forex market forecasting with two-layer stacked Long Short-Term Memory neural network (LSTM) and correlation analysis

Contact Info

Product

Resources

About