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Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions. This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First, it compares six machine learning models. These models include two very recent methods: the eXtreme Gradient Boosting (XGBoost) and CatBoost. The empirical findings indicate the superiority of XGBoost over other advanced machine learning models. Second, it proposes Shapley additive explanations (SHAP) in order to help policy makers to interpret the predictions of complex machine learning models and to examine the importance of various features that affect gold prices. Our results illustrate that the utilization of XGBoost along with SHAP approach could provide a significant boost in increasing the gold price forecasting performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models. [ABSTRACT FROM AUTHOR]

As stock data is characterized by highly noisy and non-stationary, stock price prediction is regarded as a knotty problem. In this paper, we propose new two-stage ensemble models by combining empirical mode decomposition (EMD) (or variational mode decomposition (VMD)), extreme learning machine (ELM) and improved harmony search (IHS) algorithm for stock price prediction, which are respectively named EMD–ELM–IHS and VMD–ELM–IHS. Furthermore, to demonstrate the efficiency and performance of the proposed models, the results were compared with those obtained by other methods, including EMD based ELM (EMD–ELM), VMD based ELM (VMD–ELM), autoregressive integrated moving average (ARIMA), ELM, multi-layer perception (MLP), support vector regression (SVR), and long short-term memory (LSTM) models. The results show that the proposed models have superior performance in terms of its accuracy and stability as compared to the other models. Also, we find that the sizes of sliding window and training set have a significant impact on the predictive performance. [ABSTRACT FROM AUTHOR]

Since the last two decades, financial markets have exhibited several transformations owing to recurring crises episodes that has led to the development of alternative assets. Particularly, the commodity market has attracted attention from investors and hedgers. However, the operational research stream has also developed substantially based on the growth of the artificial intelligence field, which includes machine learning and deep learning. The choice of algorithms in both machine learning and deep learning is case-sensitive. Hence, AI practitioners should first attempt solutions related to machine learning algorithms, and if such solutions are unsatisfactory, they must apply deep learning algorithms. Using this perspective, this study aims to investigate the potential of various deep learning basic algorithms for forecasting selected commodity prices. Formally, we use the Bloomberg Commodity Index (noted by the Global Aggregate Index) and its five component indices: Bloomberg Agriculture Subindex, Bloomberg Precious Metals Subindex, Bloomberg Livestock Subindex, Bloomberg Industrial Metals Subindex, and Bloomberg Energy Subindex. Based on daily data from January 2002 (the beginning wave of commodity markets' financialization) to December 2020, results show the effectiveness of the Long Short-Term Memory method as a forecasting tool and the superiority of the Bloomberg Livestock Subindex and Bloomberg Industrial Metals Subindex for assessing other commodities' indices. These findings is important in term for investors in term of risk management as well as policymakers in adjusting public policy, especially during Russian-Ukrainian war. [ABSTRACT FROM AUTHOR]

Forecasting energy demand has been a critical process in various decision support systems regarding consumption planning, distribution strategies, and energy policies. Traditionally, forecasting energy consumption or demand methods included trend analyses, regression, and auto-regression. With advancements in machine learning methods, algorithms such as support vector machines, artificial neural networks, and random forests became prevalent. In recent times, with an unprecedented improvement in computing capabilities, deep learning algorithms are increasingly used to forecast energy consumption/demand. In this contribution, a relatively novel approach is employed to use long-term memory. Weather data was used to forecast the energy consumption from three datasets, with an additional piece of information in the deep learning architecture. This additional information carries the causal relationships between the weather indicators and energy consumption. This architecture with the causal information is termed as entangled long short term memory. The results show that the entangled long short term memory outperforms the state-of-the-art deep learning architecture (bidirectional long short term memory). The theoretical and practical implications of these results are discussed in terms of decision-making and energy management systems. [ABSTRACT FROM AUTHOR]

The aim of the paper is to apply some inductive learning method from examples (which gives explicit decision rules of ‘if-then’ type) to forecast the voting behaviour of individual members of the Polish Parliament. Results obtained are both interesting and promising. [ABSTRACT FROM AUTHOR]

We compare the performance of various advanced forecasting techniques, namely artificial neural networks, k-nearest neighbors, logistic regression, Naïve Bayes, random forest classifier, support vector machine, and extreme gradient boosting classifier to predict stock price movements based on past prices. We apply these methods with the high frequency data of 27 blue-chip stocks traded in the Istanbul Stock Exchange. Our findings reveal that among the selected methodologies, random forest and support vector machine are able to capture both future price directions and percentage changes at a satisfactory level. Moreover, consistent ranking of the methodologies across different time frequencies and train/test set partitions prove the robustness of our empirical findings. [ABSTRACT FROM AUTHOR]

Companies in the fashion industry are struggling with forecasting demand due to the short-selling season, long lead times between the operations, huge product variety and ambiguity of demand information. The forecasting process is becoming more complicated by virtue of evolving retail technology trends. Demand volatility and speed are highly affected by e-commerce strategies as well as social media usage regards to varying customer preferences, short product lifecycles, obsolescence of the retail calendar, and lack of information for newly launched seasonal items. Consumers have become more demanding and less predictable in their purchasing behavior that expects high quality, guaranteed availability and fast delivery. Meeting high expectations of customers' initiates with proper demand management. This study focuses on demand prediction with a data-driven perspective by both leveraging machine learning techniques and identifying significant predictor variables to help fashion retailers achieve better forecast accuracy. Prediction results obtained were compared to present the benefits of machine learning approaches. The proposed approach was applied by a leading fashion retail company to forecast the demand of newly launched seasonal products without historical data. [ABSTRACT FROM AUTHOR]