INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
www.ijltemas.in Page 235
Forecasting Precious Metal Prices Using Simulated Data: A
Comparative Study Using MLP, ARIMA and SVR
Seema Dokrimare*, Yash Chaudhari, Anushree Sambarkar, Rajni Tupkar
Department of Statistics, Dr. D. Y. Patil, Arts, Commerce & Science College, Pimpri, Pune-18, Maharashtra, India
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1413SP048
Received: 26 June 2025; Accepted: 30 June 2025; Published: 27 October 2025
Abstract: Forecasting of precious metal prices accurately is of crucial importance of an informed financial decision-making,
robust risk mitigation and strategic asset allocation. This study represents a comparative analysis of time series forecasting
methodologies including — Autoregressive Integrated Moving Average (ARIMA), Multilayer Perceptron (MLP), and Support
Vector Regression (SVR) applied to the monthly historical datasets of gold and silver prices. These datasets were generated using
OpenAI’s ChatGPT for academic purposes. These datasets are simulated and do not directly reflect real-world market data unless
otherwise data is validated. Each of the models is evaluated over a 24-month out-of-sample forecasting horizon using rigorous
statistical metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage
Error (MAPE). The empirical findings underscore the comparative advantages of data-driven machine learning approaches,
particularly in capturing nonlinear and volatile dynamics, with MLP and SVR outperforming ARIMA in most scenarios. These
results emphasize the increasing relevance of advanced machine learning techniques in financial time series modelling.
Keywords: Simulated Data, Comparative Study, Metal, MLP, ARIMA, SVR, Time Series, forecasting
I. Introduction
The predictive modelling of precious metal prices, specifically those of gold and silver, plays a very important role in financial
analysis because of their intrinsic value, function as hedging instruments, and sensitivity to macroeconomic fluctuations. These
asset classes are subject to a multitude of exogenous variables, including global monetary policies, inflationary trends, currency
exchange volatility, and geopolitical developments. Their inherently non-stationary and nonlinear behaviors present significant
methodological challenges for forecasters.
Traditional econometric approaches, such as the Autoregressive Integrated Moving Average (ARIMA) model, have served as
foundational tools in time series forecasting, primarily because of their interpretability and effectiveness in modeling linear
temporal dependencies. However, their capacity to address structural breaks and nonlinearities is limited, rendering them less
effective in turbulent or highly dynamic market environments.
Recent advancements in computational intelligence have introduced powerful machine learning algorithms can uncover complex
patterns in financial time series. Neural networks, especially Multilayer Perceptron’s (MLPs), have demonstrated the ability to
model long-range dependencies and intricate nonlinear relationships. Similarly, Support Vector Regression (SVR) offers strong
generalization performance, particularly in high-dimensional and small-sample scenarios, making it a valuable tool for financial
forecasting. Scientifically compare the forecasting efficacy of ARIMA, MLP, and SVR models is the main aim of this study when
applied to gold and silver price series. Through rigorous experimentation and quantitative evaluation using RMSE, MAE, and
MAPE, the objective is to identify the relative strengths and limitations of each modeling paradigm, ultimately contributing to
more deep intelligence tools in the domain of financial econometrics.
II. Methodology
The analytical framework involves the deployment of ARIMA, MLP, and SVR models on monthly historical price data of gold
and silver. Data preprocessing constitutes the initial phase, encompassing the imputation of missing values through interpolation
techniques and transformation of the series to achieve stationarity. Stationarity is verified via the Augmented Dickey-Fuller
(ADF) test, and model specification for ARIMA is guided by Autocorrelation Function (ACF) and Partial Autocorrelation
Function (PACF) diagnostics. The dataset is partitioned into training and test subsets in an 80:20 ratio, with a forecast horizon
spanning 24 months.
The ARIMA model is parameterized using the auto. Arima () function from R’s forecast package, which selects optimal (p, d, q)
values by minimizing the Akaike Information Criterion (AIC). Differencing is applied where necessary, and residual
diagnostics—including the Ljung-Box test—are conducted to assess model adequacy. For linear modelling performance ARIMA
serves as a benchmark.
For the MLP model, implemented via the Keras or nnet packages in R, the time series was normalized using Min-Max scaling to
facilitate neural learning. The lagged observations were structured in a supervised learning format. The Model architecture,
including the number of hidden layers and neurons, was iteratively tuned. Techniques such as early stopping and dropout were
employed to mitigate over fitting. After prediction, outputs were rescaled to the original price range.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
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The SVR model was constructed using the e1071 or kernlab packages. Similar to MLP, lag-based feature engineering and
normalization are applied. A sliding window framework generates training sequences. Hyper parameter tuning for cost (C),
epsilon (ε), and kernel function (typically radial basis function) is performed via cross-validation. Particularly for nonlinear
regression tasks SVRs performs well by maintaining equilibrium between over fitting and generalization.
The forecast performance was assessed using RMSE, MAE, and MAPE. Visualizations accompany the forecasted outputs,
juxtaposed against historical observations, and include 95% confidence intervals to enhance interpretability.
In this study, the performance of the ARIMA, MLP, and SVR models is assessed using three commonly employed accuracy
metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). These
metrics help evaluate the accuracy and reliability of the forecasted values compared to the actual observed prices of gold and
silver over a 24-month forecast period.
Accuracy measures:
Root Mean Square Error (RMSE):
RMSE indicates how large the prediction errors are, on average, by taking the square root of the average squared differences
between the forecasted and true values. The lower the RMSE, the better the model’s predictions are aligned with the actual data.
Mathematically, it is expressed as:
Mean Absolute Error (MAE):
MAE represents the average of the absolute differences between predicted and actual values, offering a simple metric for predicting
the error. It is easier to interpret than RMSE as it measures the average magnitude of errors without considering their direction
(positive or negative). I
Mean Absolute Percentage Error (MAPE):
MAPE provides a percentage measure of prediction accuracy, which is useful for comparing forecasting performance across
different datasets or models. It is the mean of the absolute percentage error between the actual and predicted values:
A lower MAPE indicates a higher degree of accuracy in the model’s forecasts.
These three metrics were selected for their ability to capture different aspects of model performance: RMSE is sensitive to larger
errors, MAE provides a more straightforward error measurement, and MAPE offers a normalized error measurement in percentage
terms. By comparing these metrics across the ARIMA, MLP, and SVR models, we can comprehensively evaluate the relative
strengths and weaknesses of each method in forecasting precious metal prices
III. Result and Discussion
A comparative performance analysis of the ARIMA, MLP, and SVR models revealed distinct strengths across various forecast
horizons and data behaviors. All models were trained on 80% of the dataset and tested on the remaining 20% under identical pre-
processing conditions.
The ARIMA model demonstrated satisfactory performance in modeling linear patterns and seasonality, particularly when the
series was properly differenced to attain stationarity. However, it exhibited diminished accuracy during periods of abrupt market
shifts, suggesting its limitations in modeling nonlinear phenomena.
In contrast, the MLP model, once optimally tuned, displayed superior performance in both the training and test sets, particularly
in terms of RMSE and MAE. Its ability to capture higher-order dependencies and complex temporal dynamics allows it to
effectively model both short-term fluctuations and long-term trends. Notably, MLP maintained robust performance during volatile
market phases, highlighting its adaptability.
The SVR model also yielded competitive results. Its kernel-based approach allowed for effective nonlinear approximation with
minimal over fitting. Although its training error was occasionally higher than that of MLP, SVR consistently outperformed
ARIMA across all evaluation metrics. It was particularly adept at modeling moderate nonlinearities and exhibits high stability
across varying market regimes.
Graphical comparisons further validate the numerical findings. ARIMA forecasts appeared smoothed and occasionally lagged rapid
price transitions. Conversely, MLP and SVR forecasts more accurately mirrored the dynamic structure of the underlying series. The
confidence intervals for MLP and SVR forecasts were narrower, indicating higher model confidence and reduced variance.
These outcomes substantiate the hypothesis that machine learning models, particularly neural networks, and support vector-based
approaches, offer distinct advantages over traditional statistical techniques in modeling financial time series characterized by
volatility and nonlinearity.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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Table 1 Performance Evaluation Table: Comparison of ARIMA, MLP and SVR Model:
Commodity ARIMA MLP SVR
GOLD
RMSE 59.55782 660.8597 215.05
MAE 43.34726 551.5852 178.70
MAPE 3.738392 21.5629 9.18
SILVER
RMSE 1.93955 6.917 9.15
MAE 1.242198 5.606 8.13
MAPE 6.613593 18.76 31.26
The significance of the “bold” is indicated as smallest value.
Table 2 Forecasting using the ARIMA Model for Gold: The Forecasted Values for year 2025 and 2026 are as follows:
Month Forecast Lower
Bound (95%)
Upper
Bound (95%)
Month Forecast Lower
Bound (95%)
Upper
Bound (95%)
Apr-25 3017.566 2900.239 3134.894 Apr-26 3128.667 2738.879 3518.456
May-25 3026.825 2867.828 3185.821 May-26 3137.925 2733.637 3542.214
Jun-25 3036.083 2844.266 3227.9 Jun-26 3147.184 2728.898 3565.47
Jul-25 3045.341 2825.551 3265.132 Jul-26 3156.442 2724.612 3588.273
Aug-25 3054.6 2810.014 3299.186 Aug-26 3165.701 2720.738 3610.663
Sep-25 3063.858 2796.77 3330.947 Sep-26 3174.959 2717.241 3632.677
Oct-25 3073.117 2785.279 3360.954 Oct-26 3184.217 2714.09 3654.345
Nov-25 3082.375 2775.187 3389.563 Nov-26 3193.476 2711.258 3675.693
Dec-25 3091.633 2766.244 3417.023 Dec-26 3202.734 2708.722 3696.746
Jan-26 3100.892 2758.266 3443.518 Jan-27 3211.993 2706.461 3717.524
Feb-26 3110.15 2751.114 3469.186 Feb-27 3221.251 2704.457 3738.045
Mar-26 3119.409 2744.681 3494.136 Mar-27 3230.509 2702.693 3758.326
Table represents forecast with 95% confidence interval for Gold.
Fig.1. Forecast -plot using 95% confidence interval for Gold.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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Table 3 Forecasting using ARIMA Model for Silver: The Forecasted Values for years 2025 and 2026 are as follows
Month Forecast Lower Bound
(95%)
Upper Bound
(95%)
Month Forecast Lower Bound
(95%)
Upper Bound
(95%)
Apr-25 33.95266 30.1981 37.70722 Apr-26 33.54044 21.97167 45.10921
May-25 33.73406 28.65191 38.81621 May-26 33.81972 21.85504 45.7844
Jun-25 33.39379 27.4625 39.32472 Jun-26 33.61078 21.21559 46.00597
Jul-25 34.05733 27.4059 40.70887 Jul-26 33.67836 20.91101 46.4457
Aug-25 33.39342 25.9324 40.85411 Aug-26 33.75398 20.60121 46.90676
Sep-25 33.79215 25.7899 41.79441 Sep-26 33.5909 20.06287 47.11893
Oct-25 33.77081 25.12185 42.41976 Oct-26 33.76065 19.88719 47.63412
Nov-25 33.47345 24.27585 42.67105 Nov-26 33.6533 19.41594 47.89065
Dec-25 33.91384 24.23087 43.59681 Dec-26 33.66644 19.09609 48.23679
Jan-26 33.53199 23.30298 43.761 Jan-27 33.73599 18.83093 48.64104
Feb-26 33.71304 23.05403 44.37204 Feb-27 33.62629 18.38956 48.86302
Mar-26 33.76943 22.6393 44.89902 Mar-27 33.72512 18.17699 49.27325
The Table represents the forecast with 95% confidence interval for Silver
Fig.2. Forecast plot using 95% confidence interval for Silver.
IV. Conclusion:
This study provides a comparative evaluation of ARIMA, MLP, and SVR models for forecasting precious metal prices. While
ARIMA remains a foundational model for linear forecasting, its limitations are evident in dynamic, nonlinear market contexts.
The MLP model leveraging deep learning structures, demonstrated superior accuracy and robustness, particularly in volatile
environments. SVR also performed commendably, offering a parsimonious yet effective alternative for capturing nonlinearity
with strong generalization. Across all performance metrics—RMSE, MAE, and MAPE—MLP and SVR models consistently
surpassed ARIMA, affirming the value of integrating machine learning into financial forecasting. Future work may involve hybrid
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Special Issue | Volume XIV, Issue XIII, October 2025
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ensemble approaches, integration of exogenous macroeconomic indicators, and the application of more sophisticated deep learning
architectures such as LSTM or Transformer models to further enhance forecast precision in financial time series analysis.
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