Non-linear analysis of geomagnetic time series from Etna volcano

International audienceAn intensive nonlinear analysis of geomagnetic time series from the magnetic network on Etna volcano was carried out to investigate the dynamical behavior of magnetic anomalies in volcanic areas. The short-term predictability of the geomagnetic time series was evaluated to establish a possible low-dimensional deterministic dynamics. We estimated the predictive ability of both a nonlinear forecasting technique and a global autoregressive model by comparing the prediction errors. Our findings highlight that volcanomagnetic signals are the result of complex processes that cannot easily be predicted. There is slight evidence based on nonlinear predictions, that the geomagnetic time series are to be governed by many variables, whose time evolution could be better regarded as arising from complex high dimensional processes

Non-linear analysis of geomagnetic time series from Etna volcano

An intensive nonlinear analysis of geomagnetic time series from the magnetic network on Etna volcano was carried out to investigate the dynamical behavior of magnetic anomalies in volcanic areas. The short-term predictability of the geomagnetic time series was evaluated to establish a possible low-dimensional deterministic dynamics. We estimated the predictive ability of both a nonlinear forecasting technique and a global autoregressive model by comparing the prediction errors. Our findings highlight that volcanomagnetic signals are the result of complex processes that cannot easily be predicted. There is slight evidence based on nonlinear predictions, that the geomagnetic time series are to be governed by many variables, whose time evolution could be better regarded as arising from complex high dimensional processes

ANALYSIS OF STOCK PRICE PREDICTION USING DATA MINING APPROACH

Financial forecasting is one of the most interesting subjects within the area of machine learning studies. Forecasting stock prices is challenging due to the nature of stock prices that are usually non-linear, complex and noisy. This paper would be discussing the most prominent forecasting method which is the time-series forecasting and its machine learning tools used to create the prediction. The aim of this project is to study the data mining approach on predicting stock price that offers accuracy and sustains its reliability in the system. Using Data Mining approach in training the algorithms that will produce the best results based on Public Listed Companies‟ stock price data that dates back until 1998. This system utilizes Artificial Neural Network and Support Vector Machine as its main inference engine with numerous methods to measure the accuracy of both. It is anticipated that this analysis would become a platform for producing a prediction application that is reliable for usage in the future

Forecasting industrial production indices with a new singular spectrum analysis forecasting algorithm

Existing time series analysis and forecasting approaches struggle to produce accurate results in application to time series with complex trend, such as those commonly displayed by indices of industrial production (IIPs). In this study, a new version of the Singular Spectrum Analysis (SSA) technique is developed, namely the Separate Trend and Seasonality (SSA‑STS) forecasting algorithm. Its performance is compared to those of benchmark, classical times series forecasting methods, including Basic SSA (the core version of SSA), ARIMA, Exponential Smoothing (ETS) and Neural Network (NN). The methods in this study are applied to both simulated and real data. The latter includes twenty four monthly series of seasonally unadjusted IIPs of various sectors for the UK, Germany and France. Using the out-of-sample forecasts, the results of this newly developed SSA‑STS algorithm were compared to the other aforementioned forecasting schemes by the means of pooled Root-Mean-Square-Error (RMSE). The pooling is done based on the number of steps ahead the forecasts extend, allowing for the performance of the methods to be evaluated on short and long horizons. The Kolmogorov–Smirnov Predictive Accuracy (KSPA) statistical test is applied to certify whether the errors produced by SSA‑STS are statistically significantly smaller than those of all the benchmark methods. Since this new technique is based on separate trend and seasonality forecasting, it overcomes the difficulties in forecasting series with complex trends and seasonality, thus demonstrating a clear advantage over other methods in such particular cases

АРХІТЕКТУРА НЕЙРОМЕРЕЖЕВОГО КОМПЛЕКСУ ДЛЯ ПРОГНОЗУВАННЯ ЧАСОВИХ ПОСЛІДОВНОСТЕЙ НА ОСНОВІ НЕЙРОМЕРЕЖЕВОГО СПЕКТРАЛЬНОГО АНАЛІЗУ

The architecture of the neural network complex for prediction of time sequences is developed based on neural network spectral analysis. The advantages of the neural network are given spectral analysis in comparison with existing methods of singular spectral analysis. The approach to forecasting the trend and other components of the time series, which characterizes it is characterized by increased precision and stability of the forecast. The variant of improvement of the architecture-tours with neural networks of generalized regression. The neural network complex developed before it is entirely applicable for the analysis and forecasting of non-stationary processes occurring in complex dynamic systems.Розроблено архітектуру нейромережевого комплексу для прогнозування часових послідовностей на основі нейромережевого спектрального аналізу. Наведено переваги нейромережевого спектрального аналізу в порівнянні з існуючими методами сингулярного спектрального аналізу. Запропоновано підхід до прогнозування тренду та інших складових часового ряду, який характеризується підвищеною точністю та стабільністю прогнозу. Подано варіант удосконалення архітектури за допомогою нейромереж узагальненої регресії. Розроблений нейромережевий комплекс доцільно застосовувати для аналізу та прогнозування нестаціонарних процесів, що протікають в складних динамічних системах

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework

[EN] Epidemiology-based models have shown to have successful adaptations to deal with challenges coming from various areas of Engineering, such as those related to energy use or asset management. This paper deals with urban water demand, and data analysis is based on an Epidemiology tool-set herein developed. This combination represents a novel framework in urban hydraulics. Specifically, various reduction tools for time series analyses based on a symbolic approximate (SAX) coding technique able to deal with simple versions of data sets are presented. Then, a neural-network-based model that uses SAX-based knowledge-generation from various time series is shown to improve forecasting abilities. This knowledge is produced by identifying water distribution district metered areas of high similarity to a given target area and sharing demand patterns with the latter. 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