Spot Price Prediction Research Articles

Methodology for Multi-Step Forecasting of Electricity Spot Prices Based on Neural Networks Applied to the Brazilian Energy Market

Forecasting electricity spot prices holds paramount significance for informed decision-making among energy market stakeholders. This study introduces a methodology utilizing a multilayer perceptron (MLP) neural network for multivariate electricity spot price prediction. The model underwent a feature selection process to identify the most influential predictors. In the validation phase, the model’s performance was evaluated using key metrics, including trend accuracy percentage index (TAPI), normalized root mean squared error (NRMSE), and mean absolute percentage error (MAPE). The results were obtained for a four-week forecast horizon in order to serve as an auxiliary tool to facilitate decision-making processes in the short-term energy market. The relevance of short-term electricity spot price forecasting lies in its direct impact on pricing strategies during energy contract negotiations, which allows for the making of assertive decisions in the energy trading landscape.

Amazon EC2 spot price prediction using LSTM time series prediction model

Amazon EC2 spot price prediction using LSTM time series prediction model

Amazon EC2 Spot Price Prediction Using LSTM Time Series Prediction Model

Amazon EC2 Spot Price Prediction Using LSTM Time Series Prediction Model

Prediksi Spot Price Komoditas Emas Berjangka dengan Pendekatan Vector Error Correction Model

Time series data usually exhibit non-stationary behavior and involve interrelated variables. Thus, we need a model that can obtain good forecasting results from non-stationary time series data with multivariate variables. The Vector Error Correction Model (VECM) is a multivariate time series model which is a vector form of Vector Autoregressive Regression (VAR) for time series data that are non-stationary and have a cointegration relationship. This research was conducted to model the cointegration relationship in providing clarity on the long-term relationship of the influence of future prices and the Covid-19 pandemic on price movements of gold futures commodities and to predict spot price prediction modeling for gold futures commodities. The results of the research using the VECM (2) model, which is the best model, show that the future price of the gold commodity is quite dominant in influencing the value of the spot price of gold. The Covid-19 variable does not have a significant effect on the spot gold price variable.

Natural gas spot price prediction research under the background of Russia-Ukraine conflict - based on FS-GA-SVR hybrid model

The ongoing Russia-Ukraine conflict has led to significant upheaval in the worldwide natural gas sector. Accurate natural gas price forecasting, as an essential tool for mitigating market uncertainty, plays a crucial role in commodity trading and regulatory decision-making. This study aims to develop a hybrid forecasting model, the FS-GA-SVR model, which integrates feature selection (FS), genetic algorithm (GA), and support vector regression (SVR) to investigate Henry Hub natural gas price prediction amidst the Russia-Ukraine conflict. The results show that: (1) The feature selection automates model input variable selection, decreasing the time required while improving the model's accuracy. (2) The use of genetic algorithm for selecting support vector regression hyperparameters significantly improves the accuracy of natural gas price predictions. The algorithm leads to a decrease of approximately 70% in measurement indicators. (3) During the Russia-Ukraine conflict, the FS-GA-SVR hybrid model demonstrates more consistent and accurate predictions for natural gas spot prices than the base SVR model. This study serves as a valuable theoretical reference for energy policymakers and natural gas market investors worldwide, supporting their ability to anticipate fluctuations in natural gas prices.

Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?

A reliable crude oil price forecast is important for market pricing. Despite the widespread use of ensemble empirical mode decomposition (EEMD) in financial time series forecasting, the one-time decomposition on the entire time series leads the in-sample data to be affected by the out-of-sample data. Consequently, the forecasting accuracy is overstated. This study incorporates a rolling window into two prevalent EEMD-based modeling paradigms, namely decomposition-ensemble and denoising, to ensure that only in-sample time series is processed by EEMD and used for model training. Given the time-consuming process of stepwise preprocessing and model fitting, two non-iterative machine learning algorithms, random vector functional link (RVFL) neural network and extreme learning machine (ELM), are used as predictors. Hence, we develop the rolling decomposition-ensemble and rolling denoising paradigms, respectively. Contrary to the majority of prior studies, empirical results based on monthly spot price time series for the Brent and West Texas Intermediate (WTI) markets indicate that EEMD plays a weak role in improving crude oil price forecasts when only the in-sample set is preprocessed. This is compatible with the weak form of the efficient market hypothesis (EMH). Nevertheless, the suggested rolling EEMD-denoising model has an advantage over other employed models for long-term forecasting.

Optimal prediction of cloud spot instance price utilizing deep learning

Cloud platforms often offer a variety of virtual machine (VM) models of various types and capacities, enabling users to choose the instances that best meet their requirements. Cloud providers have devised systems to make the most of their redundant computing resources. The cost fluctuates dynamically based on supply and demand. "Spot price" is a common term for this. To be able to use this instance, the user must create a suitable offer above the spot price. Accurate spot price prediction allows users to pre-prepare bid prices and run time to increase the reliability of the method. For this purpose, we consider Amazon EC2 as a testbed and use its spot price history to predict the future price by constructing a proposed modified gated recurrent unit (MGRU) model and providing a proposed dropout method. Compared with other sophisticated methods, test results show that the proposed method works superior and more accurately.

A Hybrid Model for China’s Soybean Spot Price Prediction by Integrating CEEMDAN with Fuzzy Entropy Clustering and CNN-GRU-Attention

China’s soybean spot price has historically been highly volatile due to the combined effects of long-term massive import dependence and intricate policies, as well as inherent environmental elements. The accurate prediction of the price is crucial for reducing the amount of soybean-linked risks worldwide and valuable for the long-term sustainability of global agriculture. Therefore, a hybrid prediction model that combines component clustering and a neural network with an attention mechanism has been developed. After fully integrated complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) processing of the price series, the fuzzy entropy of each component is measured as the complexity characteristic. K-means clustering and reconstruction are applied to the components before being input to the CNN-GRU-Attention network for prediction to improve the model ability and adaptability of the sequences. In the empirical analysis, the proposed model outperforms other decomposition techniques and machine learning algorithms regarding prediction accuracy. After applying the decomposition part, the results have RMSE, MAPE, and MAE values of 49.59%, 22.58%, and 21.99% lower than those of the individual prediction part, respectively. This research presents a novel approach for market participants in the soybean industry for risk response. It gives a new perspective on agricultural product prices in sustainable agricultural marketing, while also providing practical tools for developing public policies and decision-making.

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system (AIS) and ant colony optimization (ACO) approaches are utilized to cultivate RBFnet for function approximation issue. The proposed hybridization of AIS and ACO approaches optimization (HIAO) algorithm combines the complementarity of exploitation and exploration to realize problem solving. It allows the solution domain having the advantages of intensification and diversification, which further avoids the situation of immature convergence. In addition, the empirical achievements have confirmed that the HIAO algorithm not only obtained the best accurate function approximation for theoretically standard nonlinear problems, it can be further applied on the instance solving for practical crude oil spot price prediction.

An exploration to graphics processing unit spot price prediction

An exploration to graphics processing unit spot price prediction

Cloud spot price prediction approach using adaptive neural fuzzy inference system with chaos theory

The dynamic pricing of cloud computing is a major challenge for cloud users all over the world. This challenge was first addressed by Amazon under the name of Amazon spot instance market. Cloud users can bid for a spot instance using this market and obtain the requested spot if their bids exceed a dynamically changing spot price. Amazon publicises the spot price but does not reveal how it is determined. In this paper, we perform chaotic time series analysis over the spot price trace. We also develop a chaos based adaptive neural fuzzy inference system (ANFIS) model based on phase-space vectors obtained during the phase of chaotic analysis. Next, we study the effect of chaos existence on the prediction accuracy of the spot price by comparing the proposed chaos-ANFIS model with the baseline ANFIS model (non-chaotic approach). Evaluation results show that the proposed chaos-ANFIS model yields better predictions of spot price compared to the baseline ANFIS model in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE).

Cloud spot price prediction approach using adaptive neural fuzzy inference system with chaos theory

Cloud spot price prediction approach using adaptive neural fuzzy inference system with chaos theory

Cost Optimization for Big Data Workloads Based on Dynamic Scheduling and Cluster-Size Tuning

Analytical data processing has become the cornerstone of today's businesses success, and it is facilitated by Big Data platforms that offer virtually limitless scalability. However, minimizing the total cost of ownership (TCO) for the infrastructure can be challenging. We propose a novel method to build resilient clusters on cloud resources that are fine-tuned to the particular data processing task. The presented architecture follows the infrastructure-as-a-code paradigm so that the cluster can be dynamically configured and managed. It first identifies the optimal cluster size to perform a job in the required time. Then, by analyzing spot instance price history and using ARIMA models, it optimizes the schedule of the job execution to leverage the discounted prices of the cloud spot market. In particular, we evaluated savings opportunities when using Amazon EC2 spot instances comparing to on-demand resources. The performed experiments confirmed that the prediction module significantly improved the cost-effectiveness of the solution – up to 80% savings compared to the on-demand prices, and at the worst-case, 1% more cost than the absolute minimum. The production deployments of the architecture show that it is invaluable for minimizing the total cost of ownership of analytical data processing solutions. • Extended cloud architecture that dynamically selects and schedules resources. • Evaluated several spot price prediction models to forecast future prices. • Confirmed feasibility of short-term spot price prediction through real data from AWS. • Cost savings of up to 80% compared to on-demand and within 1% of the absolute minimum.

BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market

For the benefit from accurate electricity price forecasting, not only can various electricity market stakeholders make proper decisions to gain profit in a competitive environment, but also power system stability can be improved. Nevertheless, because of the high volatility and uncertainty, it is an essential challenge to accurately forecast the electricity price. Considering that recurrent neural networks (RNNs) are suitable for processing time series data, in this paper, we propose a bidirectional long short-term memory (LSTM)-based forecasting model, BRIM, which splits the state neurons of a regular RNN into two parts: the forward states (using the historical electricity price information) are designed for processing the data in positive time direction and backward states (using the future price information available at inter-connected markets) for the data in negative time direction. Moreover, due to the fact that inter-connected power exchange markets show a common trend for other neighboring markets and can provide signaling information for each other, it is sensible to incorporate and exploit the impact of the neighboring markets on forecasting accuracy of electricity price. Specifically, future electricity prices of the interconnected market are utilized both as input features for forward LSTM and backward LSTM. By testing on day-ahead electricity prices in the European Power Exchange (EPEX), the experimental results show the superiority of the proposed method BRIM in enhancing predictive accuracy in comparison with the various benchmarks, and moreover Diebold-Mariano (DM) shows that the forecast accuracy of BRIM is not equal to other forecasting models, and thus indirectly demonstrates that BRIM statistically significantly outperforms other schemes.

Improving reliability and reducing cost of task execution on preemptible VM instances using machine learning approach

Cloud users can acquire resources in the form of virtual machines (VMs) instances for computing. These instances can be on-demand, reserved and spot instances. Spot-priced virtual machines are offered at the reduced cost compared to on-demand and reserved but are unreliable to use as their availability depends on user’s bid. To use spot instances (preemptible VMs), users have to bid for resources and trade-off between monetary cost and reliability as reliability increases with the increase in cost of execution. The cost of execution can be reduced significantly with the use of preemptible VM instances. These instances are only available until users bid higher in comparison with spot price that is fixed by the cloud providers. Hence, it becomes a critical challenge to minimize the associated cost and increases the reliability for a given deadline. In this article, an algorithm has been designed for predicting the spot price to facilitate the users in bidding. Further, a checkpointing algorithm has been proposed for saving the task’s progress at optimal time intervals by the use of the proposed spot price prediction algorithm. The proposed algorithms in the article emphasize the use of preprocessed data for prediction of prices in short intervals. The prediction algorithm is based on machine learning techniques. It predicts the price and provides a comprehensive comparison for prediction of the prices for long term (12 h) as well as short term (10 min). For predicting the long-term and short-term prices, different machine learning techniques have been used on the basis of least error in prediction. The best suitable machine learning algorithm with least error is selected for prediction as well as checkpointing. Using these algorithms, one can improve reliability and reduce cost of computing on preemptible VM instances significantly. To the best of our knowledge, this is the first attempt of its kind in this field.

Computing electricity spot price prediction intervals using quantile regression and forecast averaging

We examine possible accuracy gains from forecast averaging in the context of interval forecasts of electricity spot prices. First, we test whether constructing empirical prediction intervals (PI) from combined electricity spot price forecasts leads to better forecasts than those obtained from individual methods. Next, we propose a new method for constructing PI--Quantile Regression Averaging (QRA)--which utilizes the concept of quantile regression and a pool of point forecasts of individual (i.e. not combined) models. While the empirical PI from combined forecasts do not provide significant gains, the QRA-based PI are found to be more accurate than those of the best individual model--the smoothed nonparametric autoregressive model.

SPOT PRICE PREDICTION FOR CLOUD COMPUTINGUSING NEURAL NETWORKS

Advances in service-oriented architectures, virtualization, high-speed networks, and cloud computing has resulted in attractive pay-as-you-go services. Job scheduling on such systems results in commodity bidding for computing time. Amazon institutionalizes this bidding for its Elastic Cloud Computing (EC2) environment. Similar bidding methods exist for other cloud-computing vendors as well as multi–cloud and cluster computing brokers such as SpotCloud. Commodity bidding for computing has resulted in complex spot price models that have ad-hoc strategies to provide demand for excess capacity. In this paper we will discuss vendors who provide spot pricing and bidding and present the predictive models for future short-term and middle-term spot price prediction based on neural networks giving users a high confidence on future prices aiding bidding on commodity computing.