Locally adaptive factor processes for multivariate time series

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such time-varying smoothness is not accounted for, one can obtain misleading inferences and predictions, with over-smoothing across erratic time intervals and under-smoothing across times exhibiting slow variation. This can lead to mis-calibration of predictive intervals, which can be substantially too narrow or wide depending on the time. We propose a locally adaptive factor process for characterizing multivariate mean-covariance changes in continuous time, allowing locally varying smoothness in both the mean and covariance matrix. This process is constructed utilizing latent dictionary functions evolving in time through nested Gaussian processes and linearly related to the observed data with a sparse mapping. Using a differential equation representation, we bypass usual computational bottlenecks in obtaining MCMC and online algorithms for approximate Bayesian inference. The performance is assessed in simulations and illustrated in a financial application

Tendencias recientes en el pronóstico de series de tiempo financieras usando máquinas de vectores de soporte

Resumen: El pronóstico de las series de tiempo financieras es un área de trabajo intensiva para investigadores y profesionales. En este estudio, analizamos 59 artículos y discutimos sobe el progreso en el análisis de series de tiempo financieras usando máquinas de vectores de soporte. Las principales conclusiones a las que llegamos son: (a) el pronóstico se hace con datos de frecuencia diaria y los estudios con otras frecuencias de tiempo son escasos; (b) la mayoría de los artículos están enfocados en mejorar el proceso de estimación de los parámetros o en el tratamiento previo de las series de tiempo; (c) la mayor parte de los artículos se concentran en el pronóstico de un índice financiero del mercado; (d) los casos experimentales están dispersos, lo que no hace posible comparar entre diferentes estudios.Abstract: Forecasting of financial time series is an intensive working area for researchers and practitioners. In this study, we analyze 59 articles and discuss the progress in financial time series analysis using support vector machines. Our main conclusions are: (a) forecasting is doing in a daily basis and studies in other time scales are scarce; (b) most of works are devoted to improve the parameter estimation process or to preprocessing the time series; (c) most of the work is concerned to forecast market financial index; (d) experimental cases are disperse and it is no possible to compare between different studiesMaestrí

Improving forecasting accuracy of crude oil price using decomposition ensemble model with reconstruction of IMFs based on ARIMA model

The accuracy of crude oil price forecasting is more important especially for economic development and considered as the lifeblood of the industry. Hence, in this paper, a decomposition-ensemble model with the reconstruction of intrinsic mode functions (IMFs) is proposed for forecasting the crude oil prices based on the well-known autoregressive moving average (ARIMA) model. Essentially, the reconstruction of IMFs enhances the forecasting accuracy of the existing decomposition ensemble models. The proposed methodology works in four steps: decomposition of the complex data into several IMFs using EEMD, reconstruction of IMFs based on order of ARIMA model, prediction of every reconstructed IMF, and finally ensemble the prediction of every IMF for the final output. A case study was carried out using two crude oil prices time series (i.e. Brent and West Texas Intermediate (WTI)). The empirical results exhibited that the reconstruction of IMFs based on order of ARIMA model was adequate and provided the best forecast. In order to check the correctness, robustness and generalizability, simulations were carried out

Improved EMD-Based Complex Prediction Model for Wind Power Forecasting

As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this article proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Several error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.© 2020 Institute of Electrical and Electronics Engineersfi=vertaisarvioitu|en=peerReviewed

Prediction of nonlinear nonstationary time series data using a digital filter and support vector regression

Volatility is a key parameter when measuring the size of the errors made in modelling returns and other nonlinear nonstationary time series data. The Autoregressive Integrated Moving- Average (ARIMA) model is a linear process in time series; whilst in the nonlinear system, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) and Markov Switching GARCH (MS-GARCH) models have been widely applied. In statistical learning theory, Support Vector Regression (SVR) plays an important role in predicting nonlinear and nonstationary time series data. We propose a new class model comprised of a combination of a novel derivative Empirical Mode Decomposition (EMD), averaging intrinsic mode function (aIMF) and a novel of multiclass SVR using mean reversion and coefficient of variance (CV) to predict financial data i.e. EUR-USD exchange rates. The proposed novel aIMF is capable of smoothing and reducing noise, whereas the novel of multiclass SVR model can predict exchange rates. Our simulation results show that our model significantly outperforms simulations by state-of-art ARIMA, GARCH, Markov Switching generalised Autoregressive conditional Heteroskedasticity (MS-GARCH), Markov Switching Regression (MSR) models and Markov chain Monte Carlo (MCMC) regression.Open Acces

Some Essays on models in the Bond and Energy Markets

The term structure of interest rates plays a fundamental role as an indicator of economy and market trends, as well as a supporting tool for macroeconomic strategies, investment choices or hedging practices. Therefore, the availability of proper techniques to model and predict its dynamics is of crucial importance for players in the financial markets. Along this path, the dissertation initially examined the reliability of parametric and neural network models to fit and predict the term structure of interest rates in emerging markets, focusing on the Brazilian, Russian, Indian, Chines and South African (BRICS) bond markets. The focus on the BRICS is straightforward: the dynamics of their term structures make tricky the application of consolidated yield curve models. In this respect, BRICS yield curve act as stress testers. The study then examined how to apply the above cited models to energy derivatives, focusing the attention on the Natural Gas and Electricity futures, motivated by the existence of similarity. The research was carried out using ad hoc routines, such as the R package "DeRezende.Ferreira", developed by the candidate and now freely downloadable at the Comprehensive R Archive Network (CRAN) repository*, as well as by means of code written in MatLab 2021a - 2022a and Python (3.10.10) using the open-source Keras (2.4.3) library with TensorFlow (2.4.0) as backend. The dissertation consists of four chapters based on published and/or under submission materials. Chapter 1 is an excerpt of the paper • Castello, O.; Resta, M. Modeling the Yield Curve of BRICS Countries: Parametric vs. Machine Learning Techniques. Risks 2022 The work firstly offers a comprehensive analysis of the BRICS bond market and then investigates and compares the abilities of the parametric Five–Factor De Rezende–Ferreira model and Feed–Forward Neural Networks to fit the yield curves. Chapter 2 is again focused on the BRICS market but investigates a methodology to identify optimal time–varying parameters for parametric yield curve models. The work then investigates the ability of this method both for in–sample fitting and out–of–sample prediction. Various forecasting methods are examined: the Univariate Autoregressive process AR(1), the TBATS and the Autoregressive Integrated Moving Average (ARIMA) combined to Nonlinear Autoregressive Neural Networks (NAR–NN). Chapter 3 studies the term structure dynamics in the Natural Gas futures market. This chapter represents an extension of the paper • Castello, O., Resta, M. (2022). Modeling and Forecasting Natural Gas Futures Prices Dynamics: An Integrated Approach. In: Corazza, M., Perna, C., Pizzi, C., Sibillo, M. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2022. After showing that the natural gas and bond markets share similar stylized facts, we exploit these findings to examine whether techniques conventionally employed on the bonds market can be effectively used also for accurate in–sample fitting and out–of–sample forecast. We worked at first in–sample and we compared the performance of three models: the Four–Factor Dynamic Nelson–Siegel–Svensson (4F-DNSS), the Five–Factor Dynamic De Rezende–Ferreira (5F–DRF) and the B–Spline. Then, we turned the attention on forecasting, and explored the effectiveness of a hybrid methodology relying on the joint use of 4F–DNSS, 5F–DRF and B–Splines with Nonlinear Autoregressive Neural Networks (NAR–NNs). Empirical study was carried on using the Dutch Title Transfer Facility (TTF) daily futures prices in the period from January 2011 to June 2022 which included also recent market turmoil to validate the overall effectiveness of the framework. Chapter 4 analyzes the predictability of the electricity futures prices term structure with Artificial Neural Networks. Prices time series and futures curves are characterized by high volatility which is a direct consequence of an inelastic demand and of the non–storable nature of the underlying commodity. We analyzed the forecasting power of several neural network models, including Nonlinear Autoregressive (NAR–NNs), NAR with Exogenous Inputs (NARX–NNs), Long Short–Term Memory (LSTM–NNs) and Encoder–Decoder Long Short–Term Memory Neural Networks (ED–LSTM–NNs). We carried out an extensive study of the models predictive capabilities using both the univariate and multivariate setting. Additionally, we explored whether incorporating various exogenous components such as Carbon Emission Certificates (CO2) spot prices, as well as Natural Gas and Coal futures prices can lead to improvements of the models performances. The data of the European Energy Exchange (EEX) power market were adopted to test the models. Chapter 4 concludes. ____________________________ * https://cran.r-project.org/web/packages/DeRezende.Ferreira/index.htm

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance