Fractional ARIMA with an improved cuckoo search optimization for the efficient Short-term power load forecasting

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An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Enhanced recurrent neural network for short-term wind farm power output prediction

Scientists, investors and policy makers have become aware of the importance of providing near accurate spatial estimates of renewable energies. This is why current studies show improvements in methodologies to provide more precise energy predictions. Wind energy is tied to weather patterns, which are irregular, especially in climates with erratic weather patterns. This can lead to errors in the predicted potentials. Therefore, recurrent neural networks (RNN) are exploited for enhanced wind-farm power output prediction. A model involving a combination of RNN regularization methods using dropout and long short-term memory (LSTM) is presented. In this model, the regularization scheme modifies and adapts to the stochastic nature of wind and is optimised for the wind farm power output (WFPO) prediction. This algorithm implements a dropout method to suit non-deterministic wind speed by applying LSTM to prevent RNN from overfitting. A demonstration for accuracy using the proposed method is performed on a 14-turbines wind farm. The model out performs the ARIMA model with up to 80% accuracy

Artificial intelligence in wind speed forecasting: a review

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights into the data. In this paper, we explore the structure of LSTM recurrent neural networks to learn variable-wise hidden states, with the aim to capture different dynamics in multi-variable time series and distinguish the contribution of variables to the prediction. With these variable-wise hidden states, a mixture attention mechanism is proposed to model the generative process of the target. Then we develop associated training methods to jointly learn network parameters, variable and temporal importance w.r.t the prediction of the target variable. Extensive experiments on real datasets demonstrate enhanced prediction performance by capturing the dynamics of different variables. Meanwhile, we evaluate the interpretation results both qualitatively and quantitatively. It exhibits the prospect as an end-to-end framework for both forecasting and knowledge extraction over multi-variable data.Comment: Accepted to International Conference on Machine Learning (ICML), 201

Crude oil price forecasting based on the reconstruction of imfs of decomposition ensemble model with arima and ffnn models

The development of economic and industry depend upon how well the accuracy of crude oil price forecasting is managed. The study aims to reduce computation complexity and enhance forecasting accuracy of decomposition ensemble model. The propose model comprises four steps which are (i) decomposing the complex data into several IMFs using ensemble empirical mode decomposition (EEMD) method, (ii) reconstructing the decomposed IMFs through autocorrelation into stochastic and deterministic components, (iii) forecasting every reconstructed component, and (iv) ensemble all forecasted components for the final output. IMFs in the stochastic component are analysed separately. The findings confirm that the stochastic component contributed more variation as compared to deterministic component. For verification and illustration, Brent, West Texas Intermediate (WTI) daily, weekly, monthly and yearly, and Pakistan monthly spot crude oil prices were used as sample study. The empirical results indicated that the proposed model statistically outperformed all the considered benchmark models including the most popular auto-regressive integrated moving average (ARIMA) model, feed forward neural network (FFNN) model, decomposition ensemble model (EEMD-ARIMA and EEMD-FFNN), reconstruction decomposition ensemble model with stochastic and deterministic components (EEMD-(S+D)-ARIMA and EEMD- (S+D)-FFNN) and Rios and De Mello (RD) reconstruction decomposition ensemble model with stochastic and deterministic components (EEMD-RD-ARIMA and EEMD-RD-FFNN). To determine the performance, two descriptive statistical measures were applied, including the root mean square error (RMSE) and mean absolute percentage error (MAPE). The MAPE of the proposed EEMD-individual stochastic and deterministic (ISD)-FFNN model for daily and weekly data of Brent and WTI are <1%, however, for monthly Brent, WTI and Pakistan data are <5% shows a good fit produce by EEMD-ISD-FFNN. The MAPE of the model EEMDISD- FFNN for yearly Brent data is <30% indicate a reasonable fit and for WTI <20% implies a good fit. Whereas the MAPE of the EEMD-(S+D)-FFNN model for Brent yearly data <20% display a good fit and for WTI data <10% indicate excellent fit. In nutshell, the recommended model for yearly data is EEMD-(S+D)-FFNN. In conclusion, the proposed method of reconstruction of IMFs based on autocorrelation enhanced the forecasting accuracy of the EEMD model

Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Cryptocurrency Exchange Rate in High Volatility Environment: A New Insight of Bitcoin Transaction

The cryptocurrency is a decentralized digital money. Bitcoin is a digital asset designed to work as a medium of exchange using cryptography to secure the transactions, to control the creation of additional units, and to verify the transfer of assets. The objective of this study is to forecast Bitcoin exchange rate in high volatility environment. Methodology implemented in this study is forecasting using autoregressive integrated moving average (ARIMA). This study performed autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis in determining the parameter of ARIMA model. Result shows the first difference of Bitcoin exchange rate is a stationary data series. The forecast model implemented in this study is ARIMA (2, 1, 2). This model shows the value of R-squared is 0.444432. This value indicates the model explains 44.44% from all the variability of the response data around its mean. The Akaike information criterion is 13.7805. This model is considered a model with good fitness. The error analysis between forecasting value and actual data was performed and mean absolute percentage error for ex-post forecasting is 5.36%. The findings of this study are important to predict the Bitcoin exchange rate in high volatility environment. This information will help investors to predict the future exchange rate of Bitcoin and in the same time volatility need to be monitor closely. This action will help investors to gain better profit and reduce loss in investment decision

Advanced Data Analytics Methodologies for Anomaly Detection in Multivariate Time Series Vehicle Operating Data

Early detection of faults in the vehicle operating systems is a research domain of high significance to sustain full control of the systems since anomalous behaviors usually result in performance loss for a long time before detecting them as critical failures. In other words, operating systems exhibit degradation when failure begins to occur. Indeed, multiple presences of the failures in the system performance are not only anomalous behavior signals but also show that taking maintenance actions to keep the system performance is vital. Maintaining the systems in the nominal performance for the lifetime with the lowest maintenance cost is extremely challenging and it is important to be aware of imminent failure before it arises and implement the best countermeasures to avoid extra losses. In this context, the timely anomaly detection of the performance of the operating system is worthy of investigation. Early detection of imminent anomalous behaviors of the operating system is difficult without appropriate modeling, prediction, and analysis of the time series records of the system. Data based technologies have prepared a great foundation to develop advanced methods for modeling and prediction of time series data streams. In this research, we propose novel methodologies to predict the patterns of multivariate time series operational data of the vehicle and recognize the second-wise unhealthy states. These approaches help with the early detection of abnormalities in the behavior of the vehicle based on multiple data channels whose second-wise records for different functional working groups in the operating systems of the vehicle. Furthermore, a real case study data set is used to validate the accuracy of the proposed prediction and anomaly detection methodologies

Modelling stock market exchange by autoregressive integrated moving average, multiple linear regression and neural network

Stocks, sometimes known as equities, are fractional ownership shares in a firm, and the stock market is a venue where investors may purchase and sell these investible assets. Because it allows enterprises to quickly get funds from the public, a well-functioning stock market is critical to economic progress. The purpose of this study is to model Bursa Malaysia using autoregressive integrated moving average (ARIMA), multiple linear regression (MLR), and neural network (NN) model. To compare the modelling accuracy of these models for intraday trading, root mean square error (RMSE) and mean absolute percentage error (MAPE) as well as graphical plot will be used. From the results obtained from these three methods, the NN model provides the best trade signal