Time Series Forecasting Using Bayesian Method: Application to Cumulative Rainfall

The article analyzes the application of fractal analysis and neural networks in internet technologies for diagnosing the state of a studied object and forecasting time series. These methods will expand the possibilities of using predictions for the future behavior of the researched object in the fields of finance, investment, economics, sociology, and other industries where obtaining prospective knowledge is required. The authors provide an overview of predictive analysis theories and modern econometric and mathematical methods for modeling time series. The justification for choosing fractal analysis as an effective method for diagnosing business decision-making processes is given. A methodology for using internet technology to automate time series forecasting is proposed. A web application has been developed as a software product to solve the research tasks. The process of developing the web application is examined, which would forecast the financial indicators of Ukrainian banks based on available time series. The main stages of development and architecture of the web application for diagnosing and forecasting business processes are described. A generalized structure of software modules is presented. Time series of net profit for Ukrainian banks have been selected as the base model for analysis and forecasting. The developed web application is a complex of programs that use a neural network for time series forecasting. Fractal analysis is used in the stage of forming input data for the neural network. The application also provides the ability to conduct R/S analysis of time series to determine the Hurst exponent. The software product also allows training the neural network using backpropagation. With the machine learning algorithms used in the web application, it is possible to analyze large volumes of data and discover complex dependencies and patterns in time series that may be difficult to notice manually. This leads to more accurate and faster results and enables making more objective forecasts based on the available data. The developed web application can also be used for predicting crises in financial and investment activities.

В работе предложены математические модели прогнозирования нестационарных временных рядов с учётом внешних факторов и методы их структурной идентификации, основанные на совместном использовании многомерного варианта метода «Гусеница»-SSA и моделей SARIMAX, расширенных для прогнозирования временных рядов с несколькими сезонными составляющими и с учётом нескольких экзогенных переменных. Экспериментальные результаты показывают высокую эффективность предложенных моделей прогнозирования при выборе подходящих структурных параметров в сравнении с моделями SARIMAX.

Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a forecasting competition. J. Forecasting 1 111–153.]); the traditional method forecasts were estimated by experts in the particular technique. The neural networks were estimated using the same ground rules as the competition. Across monthly and quarterly time series, the neural networks did significantly better than traditional methods. As suggested by theory, the neural networks were particularly effective for discontinuous time series.

Chapter 10 - Time Series Forecasting

Time series and regression methods for univariate environmental forecasting: An empirical evaluation

The forecasting and characterization of time series are very useful for experts to take appropriate decisions, to plan actions and to understand the time series patterns. However, there are a small number of methods that consider both objectives at the same time. In this paper, an algorithm for nonstationary and seasonal time series forecasting, with an evolving neuro-fuzzy Takagi-Sugeno (NF-TS) structure, is proposed. For this algorithm, the NF-TS inputs are unobservable patterns extracted from the time series by a decomposition technique. As experiment, a real seasonal time series was used to compare the forecasting performance of these proposed algorithm with an other similar NF-TS, whose inputs were formed by autoregressive data from the same time series. When there is available observations from time series, the NF-TS evolves its structure and adapt its parameters. If the data is not available, the proposed methodology needs to forecast the next value. In order to extract the unobservable components from time series, the Holt-Winters method optimized by Particle Swarm Optimization (PSO) approach was considered in this experiment.

A new architecture selection method based on tabu search for artificial neural networks

A contrastive learning based universal representation for time series forecasting

A fast LSH-based similarity search method for multivariate time series

The two-stage adaptive approach for time series forecasting is proposed. The first stage involves the decomposition of the initial time series into basis functions and application to them of the Hilbert transform. At the second stage the obtained functions and their instantaneous amplitudes are used as input variables of neural network forecasting. The efficiency of the developed approach is displayed in real time series in the electric power problem of forecasting the sharply variable implementations of active power flows.

Accurate emergency call demand forecasting is essential for optimizing resource allocation and response times in Emergency Medical Services (EMS). Time series forecasting, a cornerstone of machine learning, plays a crucial role in predicting demand patterns. This research proposes a novel multi-series forecasting model for scenarios involving multiple independent time series, representing call data from distinct service areas. While previous research has explored multivariate time series and machine learning methods for EMS demand forecasting, this study focuses on comparing a simplified independent time series approach against the proposed multi-series model. By evaluating the accuracy of both methods in predicting future call volumes, the findings will provide insights into the most effective forecasting approach, ultimately contributing to improved resource allocation and enhanced patient care in EMS.

The current ability to produce massive amounts of data and the impossibility in storing it motivated the development of data stream mining strategies. Despite the proposal of many techniques, this research area still lacks in approaches to mine data streams composed of multiple time series, which has applications in finance, medicine and science. Most of the current techniques for clustering streaming time series have a serious limitation in their similarity measure, which are based on the Pearson correlation. In this paper, we show the Pearson correlation is not capable of detecting similarities even for classic time series models, such as those by Box and Jenkins. This limitation motivated our proposal to cluster streaming time series based on their generating functions, which is achieved by considering features obtained using descriptive measures, such as Auto Mutual Information, the Hurst Exponent and several others. We present a new tree-based clustering algorithm, entitled TS-Stream, which uses the extracted features to produce partitions in better accordance to the time series generating functions. Experiments with synthetic data sets confirm TS-Stream outperforms ODAC, currently the most popular technique, in terms of clustering quality. Using real financial time series from the NYSE and NASDAQ, we conducted stock trading simulations employing TS-Stream to support the creation of diversified investment portfolios. Results confirmed TS-Stream increased the monetary returns in several orders of magnitude when compared to trading strategies simply based on the Moving Average Convergence Divergence financial indicator.

With the development of Wind power grid connection technology, the ultra-short-term wind speed prediction technology plays an important role in power system operation and energy scheduling. This paper proposes a median regression prediction model based on Gaussian process(GPMR). This model can accurately analyze and predict the time series of ultra-short-term wind speed from several minutes to an hour. Based on the assumption of prior distribution by Gaussian process, this method solves the problem of nonlinear processing in the conventional time series method. It also combines with the selection of median, overcoming the shortcoming that the previous mean point prediction is easily affected by large error deviation, making the prediction result more representative. In the part of example analysis, two machine learning prediction methods commonly used in recent years are compared, and the simulation results show that the method proposed in this paper is more accurate in the ultra-short-term prediction of wind speed.

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.

Copula-Based Dependence Characteriztions and Modeling for Time Series