Gold Market Analyzer using Selection based Algorithm

Gold is the most important and valuable element right from its discovery. It is the most significant element at present and also the most valuable asset to. In the present market scenario the investors use gold as the security for their shares investment. During International trade all the countries use gold as their main mode of transaction. It is seen that all the currencies accepted by the International market are having the gold as the backup for their economy. The prices of gold are rising day by day continuously. As we see in the history of gold market the present prices of gold are much high as compare to the past values and that's why the gold market has attracted the most attention. The paper focus on the continuous changing in the gold rates, investment policies depend on the forecasting of trends in gold which will help the data mining companies to minimize the risk The description of the future situation on the basis of present trends is just not limited to the forecasting the prices. The knowledge discovers by the data mining techniques is gathered from the different gold related websites and also from the jewellers database. It is much more important for the ornaments making companies to know the demand and the requirements for the ornaments during the unstable (uncertain) market conditions. For the classification purpose the maid and sale database was gathered from the nearest jewellery shops of the past 5 years. The prediction is done after complete analysis of the gathered data set. With this the paper concentrate on making the information available about the government and private schemes related to gold market on one place. The paper proposes the system that gives the total access to the registered user and limited access to the unregistered user to get the required information of gold. The latest updates are provided to the registered user by sending mail or text message when the user was offline

Prediction of the Italian electricity price for smart grid applications

In this paper we address the problem of one day-ahead hourly electricity price forecast for smart grid applications. To this aim, we investigate the application of a number of predictive models for time-series, including methods based on empirical strategies frequently adopted in the smart grid community, Kalman Filters and Echo State Networks (ESNs). The considered methods have been suitably modified to address the electricity price forecast problem. Strategies based on daily re-adaptation of models’ parameters are taken into consideration as well. The predictive performance achieved by the considered models is assessed, and the methods are compared among each other on recent real data from the Italian electricity market. As a result of the comparison over three years data, ESN methods appear to provide the most accurate price predictions, which could imply significant economic savings in many smart grid activities, such as switching on power plants to support power generation from renewable sources, electric vehicle recharging or usage of household appliances

Redes neuronales y arima para la predicción del precio de la electricidad a corto plazo

ABSTRACT: Electricity price forecasting provides significant information for the different elec tricity market agents so that their profits can be maximized. This work is meant to make a univariate and multivariate comparison between state-of-the-art statistical models such as ARIMA and Transfer Function Models, and the promising Deep Learn ing models, such as Recurrent Neural Networks and Convolutional Neural Networks, in order to make 24 hours ahead predictions of the electricity price in the Spanish elec tricity market for the 2020 timespan. In addition, an ensembling model composed of models from both backgrounds will be suggested to improve the predictions of either individual model. In the experiments, Convolutional Neural Networks outperformed all other Neural Networks at univariate and multivariate level and had similar results to the state-of-the-art statistical models at univariate level, outperforming them at mul tivariate level. Additionally, it has been shown that the ensembling model obtains considerably better results than each of the individual modelsGrado en Economí

Machine Learning Ensembles for Grid Congestion Price Forecasting

Title from PDF of title page, viewed June 21, 2023Thesis advisor: Reza DerakhshahniVitaIncludes bibliographical references (pages 54-56)Thesis (M.S.)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2023In this thesis, we embarked on a comprehensive study to develop a cutting-edge model for forecasting real-time electricity prices across 35 nodes within the PJM zone. The task at hand was particularly challenging, given the volatility of the day-ahead electricity market and the numerous factors that influence prices, such as load variations, weather conditions, and historical prices. Our objective was to devise a model that could provide more accurate day-ahead price forecasts than existing methods. To achieve this goal, we proposed an ensemble-based approach that leveraged the strengths of low-bias and high-variance machine learning models. To handle missing values, we employed K-Nearest Neighbors (KNN) imputation. To enhance the performance of the models, we employed Principal Component Analysis (PCA) and correlation feature selection techniques. We then employed a direct multi-output strategy to forecast real-time prices. Our ensemble incorporated a variety of models such as Support Vector Regression (SVR), Huber Regression, and deep neural networks such as Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Temporal Convolutional Network (TCN). Our results on test data from the first half of 2021 demonstrate that our proposed strategy outperforms any single model by 8.75% over all 35 nodes and beats the day-ahead prices. However, we noticed a decrease in testing accuracy in the latter half of 2021, indicating a need for a more dynamic ensemble fusion. In conclusion, our research provides valuable insights into electricity price forecasting and illustrates the effectiveness of ensemble learning techniques, incremental learning, and deep neural networks for time series forecasting. Our proposed method can be utilized by energy traders, independent system operators, and policymakers to make more informed decisions in the uncertain and volatile energy market.Introduction -- Literature review -- Data collection and preprocessing -- Data modellin

Wind Power Prediction with Machine Learning Methods in Complex Terrain Areas

The increasing amount of intermittant wind energy sources connected to the power grid present several challenges in balancing the power network. Accurate prediction of wind power production is identified as one of the most important measures for balancing the power network while maintaining a sustainable integration of wind power in the power grid. However, the volatile nature of wind makes wind power forecasting a complicated task, and it is known that the performance of already established wind power prediction models decreases for wind farms in complex terrain sites. This thesis aims to forecast the future wind power output for five different wind farms in Northern Norway using methods from statistics and machine learning. The wind farm sites are generally characterized as complex terrain areas with good wind resources. Four different prediction models are developed for short to medium-term, multi- step prediction of wind power, ranging from traditional statistical models such as the arimax process to complex machine learning models. Additionally, two of the models are implemented both using the recursive and the direct multi- step forecasting technique. For each wind farm, the models are evaluated for an entire year and utilize multivariate input data with variables from a nwp model. The results of the experiments varied greatly across all locations. It was seen that the implemented models were outperformed by the persistence model for short forecasting horizons. However, when the forecasting horizon increased, several models showed a lower error than the persistence model

Load Forecasting based on Deep Long Short-term Memory with Consideration of Costing Correlated Factor

Guangdong University of Technology, Guangzhou, China, Grant from the Financial and Education Department of Guangdong Province 2016[202]: Key Discipline Construction Programme; Education Department of Guangdong Province: New and integrated energy system theory and technology research group, project number 2016KCXTD022; National Science Foundation of China: A Time-Based-Demand- Response Program of Compensated Multiple-Shape Pricing Scheme, Grant No. 51707041; State Grid Technology Project: the Smart Monitoring Techniques Research in Self- Correlated Framework for Power Utility (Grant No. 5211011600RJ); Education Department of Guangdong Province: The Power Market Advanced Service for Load Monitoring Technologies, 2016KQNCX047

News Sentiment in Volatility predictions : Exploring the effect of news sentiment on stock volatility using machine learning regression models

In this thesis, we explore how sentiment from financial news could affect stock volatility. Using financial data from the S&PlO0, volatility data from the Volatility Index (VIX) and sentiment data collected with web scraping we make five different machine learning models with different covariates. Analyzing the effect in both individual sectors and a combination of all sectors, with a total of 240 different models. In order to isolate the effect of sentiment, we create datasets with and without the information and look at how the results differ. We found little proof that the additional information from the news sentiment affects the result significantly. The reason for this is complex, but we believe that using sentiment would be better suited for classification of volatility direction. Our best attempts to predict volatility on index level came from the LSTM model that got an score of 43,6% using sentiment as a covariate. The best result on an individual sector came from the random forest model that got an R2 score of 62.5% using sentiment to predict volatility in the energy sector. Although these scores isolated are acceptable, for the majority of the models, those without sentiment data performed as well, if not better.nhhma