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Predicting gold prices using machine learning can be an engaging venture, given the importance of gold in financial markets. The price of gold can be influenced by various factors, including interest rates, inflation, global economic stability, and more.
Here’s a guide to developing a gold price prediction system:
State the main goal: “To develop a machine learning model that predicts the future price of gold based on historical prices and other relevant features.”
Historical Gold Prices: Daily closing prices, opening prices, highs, lows, etc.
Macroeconomic Indicators: Interest rates, inflation rates, stock market indices, currency exchange rates, etc.
Global Events Data: Economic, political events, or crises that might influence gold prices.
Data Cleaning: Handle missing values, outliers, and potential errors in the data.
Feature Engineering: Derive features like moving averages, volatility measures, and momentum indicators.
Time Series Decomposition: Break the time series into trend, seasonal, and residual components.
Normalization/Standardization: This is particularly important if you’re using algorithms sensitive to feature scales.
Time Series Models: ARIMA, Exponential Smoothing State Space Model (ETS), and Prophet can be directly applied to time series data.
Machine Learning Models: Regression models like Linear Regression, Random Forest, Gradient Boosting, and LSTM (a type of recurrent neural network suited for time series).
Splitting the Data: Time series data should be split in a chronological order. For instance, use data until 2022 for training and post-2022 data for testing.
Cross-Validation: Use time series specific cross-validation methods, like rolling forecasts.
Training: Train the model using the training dataset, and validate its performance using metrics like RMSE, MAE, or MAPE.
Evaluate the model’s predictive performance on the test dataset.
Plot actual vs. predicted prices to visually assess performance.
Depending on the model used, adjust hyperparameters to optimize performance. For instance, the number of trees in a Random Forest, or the learning rate in Gradient Boosting.
Implement the model in a system where it can fetch the latest data, make predictions, and provide insights or alerts based on its forecasts.
Create a dashboard that visualizes historical gold prices, predicted future prices, and confidence intervals.
Provide tools to adjust parameters or input external events that might influence prices.
Summarize the project’s outcomes, challenges faced, and potential economic or strategic impact.
Consider future improvements like:
Integrating more data sources or features (e.g., data from gold mines, gold demand/supply statistics).
Using more advanced models or ensemble techniques.
Regularly retraining the model with new data.
Gold prices can be influenced by unpredictable global events, so always consider the inherent uncertainties in predictions.
Collaborate with domain experts in finance or commodities trading for richer insights.
A machine learning-based gold price prediction system can be a valuable tool for traders, investors, and financial institutions, helping them make informed decisions and hedge against risks.
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Gold price prediction, Algorithms, Machine learning, Neural networks, Support vector machines, Model selection, Decision trees
Our study did a complete survey and investigate different machine learning methods like LR, SVM, DT, Gaussian process regression, Random Forest regression, Polynomial features, Lasso regression, KNN, Ridge regression and Neural networks to predict the gold price. Our models have able to offer accurate prediction of gold price and the quality of the input data and model chosen procedure can affect how accurate these models are. Our SVM model gives the better accurate prediction.
Linear Regression, Multiple Linear Regression, ARIMA Model, Random Forest Regression
Our work uses ML methods to improve the performance of several types of activities. It is utilized to predict the financial variables with an emphasis on equities rather than commodities. We focus on how predictions are made by utilizing datasets and statistical analysis. We used ensemble ML methods like LR, ARIMA model and RF regression. Our ARIMA model gives the best performance.
Regression, Decision Tree, Ridge Regression
We used the machine learning methods like Decision Tree, Linear Regression, Random Forest Regression, SVM and Ridge Regression. We have to compare these methods with R Squared Error, Root Mean Square Error to estimate parameters. At first we collect data then preprocess the data to train and test the data. We observe that as compared to other ML methods RF method gives much accurate result.
Multilayer perceptron neural network, Predicting, Training neural network, Swarm intelligence optimizers, Harris Hawks optimization algorithm
We can predict the gold price by utilizing the MLP-NN that has optimal control parameters to be gained by utilizing enhanced version of Harris Hawks optimizer (HHO) with an external archive which is called AHHO-NN. We used two feature reduction methods to choose input for MLP-NN to predict gold price namely Pearson’s correlation and a newly proposed categorized correlation. Our AHHO-NN model is compared with swarm intelligence methods HHO-NN, JAYA-NN, MFO-NN, PSO-NN as well as four ML methods LR, MLP, RANSAC, and TR. Our AHHO-NN method gives high accuracy.
Deep Learning, LSTM, FTS
The goal of our paper is to predict the gold price by utilizing ML structure with deep learning such as Long Short Term Memory (LSTM) and Fuzzy Time Series (FTS). We have some test processes that carried-out in training process and we predict the LSTM and FTS models to obtain the best outcome. Our LSTM gives more accurate outcome than FTS.
Gold and silver price prediction, CNN, CNN-RNN, Hybrid model
We effort to estimate the potency of machine learning methods to expect the feature price of two valuable metals like gold and silver in Indian market. We execute the hybrid machine learning methods like CNN and CNN-RNN are recognised in our work. We can measure the accuracy of the model by utilizing MAPE. RNN performs well in predicting the gold price.
ANN, ANN-PSO, RNN, Gold price
We attempt to make prediction of gold price by utilizing machine learning methods. Our paper uses four Machine learning methods to analyse the data that are ANN, ANN with Particle swarm optimisation (ANN-PSO) hybrid method, RNN and a hybrid method of CNN with RNN (CNN-RNN). Our hybrid ANN-PSO method performs better when compared to other three methods.
Forecasting, Gradient Boosting Regression, Support Vector Regressor, Extra Tree Regressor.
Our paper utilizes historical time series data predict current gold price. To predict that gold price we utilized some correlation factors like copper price, silver price, standard, etc. Consider these prices of each correlation factors and gold price data. We used some ML methods to analyse time-series data are Random Forest Regression, Support vector Regressor, linear regressor, Extra trees regressor and gradient boosting regression. Extra trees regressor will give the best accuracy rate.
M5P Algorithm, Model Trees
We can improve the system that can be based on machine learning methods to predict gold price based on historical data associated with other closely related stock market indicators. Our system is based on M5P model tree machine learning methods that can utilized to train historical commodity prices that are key indicator and forms core decision logic for feature prediction. Our M5P predicts better performance when compared to others.
Gold and silver prices; forecasting; random forests; bagging; Stochastic Gradient boosting
We used some machine learning tree-based classifiers like bagging, stochastic gradient boosting, random forests to predict the gold and silver price much more accurate than logit models. To forecast the gold and silver price direction, tree bagging and random forests provide an attractive grouping of accuracy and ease of valuation. A portfolio based random forest price prediction gives best performance.
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