Browsing by Subject "Time series analysis"

Now showing 1 - 15 of 15

Open Access
Diverse relevance feedback for time series with autoencoder based summarizations
(IEEE Computer Society, 2018) Eravci, B.; Ferhatosmanoglu, H.
We present a relevance feedback based browsing methodology using different representations for time series data. The outperforming representation type, e.g., among dual-tree complex wavelet transformation, Fourier, symbolic aggregate approximation (SAX), is learned based on user annotations of the presented query results with representation feedback. We present the use of autoencoder type neural networks to summarize time series or its representations into sparse vectors, which serves as another representation learned from the data. Experiments on 85 real data sets confirm that diversity in the result set increases precision, representation feedback incorporates item diversity and helps to identify the appropriate representation. The results also illustrate that the autoencoders can enhance the base representations, and achieve comparably accurate results with reduced data sizes.
Open Access
Efficiency of the Turkish stock exchange with respect to monetary variables: a cointegration analysis
(Elsevier BV, 1996) Muradoglu, Y. G.; Metin, K.
In this study, we test the semistrong form of the efficient market hypothesis in Turkey by using the recently developed techniques in time series econometrics, namely unit roots and cointegration. The long run relationship between stock prices and inflation is investigated by assuming the possible existence of a proxy effect. Conclusions are made as to the efficiency of the Turkish Stock Exchange and its possible implications for investors. To our knowledge, this is among the pioneering studies conducted in an emerging market that uses an updated econometric methodology to allow for an analysis of long run steady state properties together with short run dynamics.
Open Access
Environment Kuznets curve for CO2 emissions: a cointegration analysis for China
(Elsevier Ltd, 2009) Jalil, A.; Mahmud, S. F.
This study examines the long-run relationship between carbon emissions and energy consumption, income and foreign trade in the case of China by employing time series data of 1975-2005. In particular the study aims at testing whether environmental Kuznets curve (EKC) relationship between CO2 emissions and per capita real GDP holds in the long run or not. Auto regressive distributed lag (ARDL) methodology is employed for empirical analysis. A quadratic relationship between income and CO2 emission has been found for the sample period, supporting EKC relationship. The results of Granger causality tests indicate one way causality runs through economic growth to CO2 emissions. The results of this study also indicate that the carbon emissions are mainly determined by income and energy consumption in the long run. Trade has a positive but statistically insignificant impact on CO2 emissions. © 2009 Elsevier Ltd. All rights reserved.
Open Access
Estimation and forecasting of PM10 air pollution in Ankara via time series and harmonic regressions
(Springer, 2020) Akdi, Y.; Okkaoğlu, Y.; Gölveren, E.; Yücel, M. Eray
In this study, monthly particulate matter (PM10) values in Ankara (39.9334° N, 32.8597° E) from January 1993 to December 2017 are examined. The PM10 are those thoracic particles whose aerodynamic diameter is less than 10 μm (micrometers), and it is of critical health importance due to the penetrability to the lower airways. As an alternative to classical unit root tests, a unit root test primarily based on periodograms is introduced owing to its advantages over alternatives. After examining the stationarity of the series through periodogram-based test as well as its standard rivals, periodic components in the series are examined and it is observed that the series has both periodic and seasonal components. These components are modeled, using the inherent dynamics of a time series alone, within a trigonometric harmonic regression setup, eventually yielding the forecast values for 2018 that turns out to be superior to those obtained by means of ARIMA (autoregressive integrated moving average). This is a striking result since the modeling framework requires no assumptions, no parameter estimations except for the variance of the white noise series, no simulations of the power of tests, no adjustments of test statistics with respect to sample size and no preliminary work as to independent variable which is simply time, i.e., the period of forecast.
Open Access
Evaluating predictive performance of judgemental extrapolations from simulated currency series
(Elsevier, 1999) Pollock, A. C.; Macaulay, A.; Önkal-Atay, D.; Wilkie-Thomson, M. E.
Judgemental forecasting of exchange rates is critical for financial decision-making. Detailed investigations of the potential effects of time-series characteristics on judgemental currency forecasts demand the use of simulated series where the form of the signal and probability distribution of noise are known. The accuracy measures Mean Absolute Error (MAE) and Mean Squared Error (MSE) are frequently applied quantities in assessing judgemental predictive performance on actual exchange rate data. This paper illustrates that, in applying these measures to simulated series with Normally distributed noise, it may be desirable to use their expected values after standardising the noise variance. A method of calculating the expected values for the MAE and MSE is set out, and an application to financial experts' judgemental currency forecasts is presented.
Open Access
Financial earthquakes, aftershocks and scaling in emerging stock markets
(Elsevier BV, 2004) Selçuk, F.
This paper provides evidence for scaling laws in emerging stock markets. Estimated parameters using different definitions of volatility show that the empirical scaling law in every stock market is a power law. This power law holds from 2 to 240 business days (almost 1 year). The scaling parameter in these economies changes after a change in the definition of volatility. This finding indicates that the stock returns may have a multifractal nature. Another scaling property of stock returns is examined by relating the time after a main shock to the number of aftershocks per unit time. The empirical findings show that after a major fall in the stock returns, the stock market volatility above a certain threshold shows a power law decay, described by Omori's law. © 2003 Elsevier B.V. All rights reserved.
Open Access
Fractional fourier transform in time series prediction
(IEEE, 2022-12-09) Koç, Emirhan; Koç, Aykut
Several signal processing tools are integrated into machine learning models for performance and computational cost improvements. Fourier transform (FT) and its variants, which are powerful tools for spectral analysis, are employed in the prediction of univariate time series by converting them to sequences in the spectral domain to be processed further by recurrent neural networks (RNNs). This approach increases the prediction performance and reduces training time compared to conventional methods. In this letter, we introduce fractional Fourier transform (FrFT) to time series prediction by RNNs. As a parametric transformation, FrFT allows us to seek and select better-performing transformation domains by providing access to a continuum of domains between time and frequency. This flexibility yields significant improvements in the prediction power of the underlying models without sacrificing computational efficiency. We evaluated our FrFT-based time series prediction approach on synthetic and real-world datasets. Our results show that FrFT gives rise to performance improvements over ordinary FT.
Open Access
Generic windowing support for extensible stream processing systems
(John Wiley & Sons Ltd., 2014) Gedik, B.
Stream processing applications process high volume, continuous feeds from live data sources, employ data-in-motion analytics to analyze these feeds, and produce near real-time insights with low latency. One of the fundamental characteristics of such applications is the on-the-fly nature of the computation, which does not require access to disk resident data. Stream processing applications store the most recent history of streams in memory and use it to perform the necessary modeling and analysis tasks. This recent history is often managed using windows. All data stream management systems provide some form of windowing functionality. Windowing makes it possible to implement streaming versions of the traditionally blocking relational operators, such as streaming aggregations, joins, and sorts, as well as any other analytic operator that requires keeping the most recent tuples as state, such as time series analysis operators and signal processing operators. In this paper, we provide a categorization of different window types and policies employed in stream processing applications and give detailed operational semantics for various window configurations. We describe an extensibility mechanism that makes it possible to integrate windowing support into user-defined operators, enabling consistent syntax and semantics across system-provided and third-party toolkits of streaming operators. We describe the design and implementation of a runtime windowing library that significantly simplifies the construction of window-based operators by decoupling the handling of window policies and operator logic from each other. We present our experience using the windowing library to implement a relational operators toolkit and compare the efficacy of the solution to an earlier implementation that did not employ a common windowing library. Copyright © 2013 John Wiley & Sons, Ltd.
Open Access
Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology
(Springer, 2021-09-03) Akdi, Y.; Gölveren, E.; Ünlü, K. D.; Yücel, Mustafa Eray
In this study, monthly particulate matter (PM2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.
Open Access
Multivariate time series imputation with transformers
(IEEE, 2022-11-25) Yıldız, A. Yarkın; Koç, Emirhan; Koç, Aykut
Processing time series with missing segments is a fundamental challenge that puts obstacles to advanced analysis in various disciplines such as engineering, medicine, and economics. One of the remedies is imputation to fill the missing values based on observed values properly without undermining performance. We propose the Multivariate Time-Series Imputation with Transformers (MTSIT), a novel method that uses transformer architecture in an unsupervised manner for missing value imputation. Unlike the existing transformer architectures, this model only uses the encoder part of the transformer due to computational benefits. Crucially, MTSIT trains the autoencoder by jointly reconstructing and imputing stochastically-masked inputs via an objective designed for multivariate time-series data. The trained autoencoder is then evaluated for imputing both simulated and real missing values. Experiments show that MTSIT outperforms state-of-the-art imputation methods over benchmark datasets.
Open Access
Short time series microarray data analysis and biological annotation
(IEEE, 2008) Sökmen, Z.; Atalay, V.; Çetin-Atalay, Rengül
Significant gene list is the result of microarray data analysis should be explained for the purpose of biological functions. The aim of this study is to extract the biologically related gene clusters over the short time series microarray gene data by applying unsupervised methods and automatically perform biological annotation of those clusters. In the first step of the study, short time series microarray expression data is clustered according to similar expression profiles. After that, several biological data sources are integrated to get information related with the genes in one of those clusters and new sub-clusters are created by using this unified information. As a last step, biological annotation of gene sub-clusters is performed by using information related with those sub-clusters.
Open Access
Time-aware and context-sensitive ensemble learning for sequential data
(Institute of Electrical and Electronics Engineers, 2023-09-26) Fazla, Arda; Aydın, Mustafa E.; Kozat, Suleyman Serdar
We investigate sequential time series data through ensemble learning. Conventional ensemble algorithms and the recently introduced ones have provided significant performance improvements in widely publicized time series prediction competitions for stationary data. However, recent studies are inadequate in capturing the temporally varying statistics for non-stationary data. To this end, we introduce a novel approach using a meta learner that effectively combines base learners in both a time varying and context-dependent manner. Our approach is based on solving a weight optimization problem that minimizes a specific loss function with constraints on the linear combination of the base learners. The constraints are theoretically analyzed under known statistics and integrated into the learning procedure of the meta-learner as part of the optimization in an automated manner. We demonstrate significant performance improvements on real-life data and well-known competition datasets over the widely used conventional ensemble methods and the state-ofthe-art forecasting methods in the machine learning literature. Furthermore, we openly share the source code of our method to facilitate further research and comparison.
Open Access
The US shale oil production, market forces and the US export ban
(Emerald, 2021-07-26) Taneri, İlayda; Doğan, N.; Berument, M. Hakan
Purpose – The purpose of this paper is to use the novel data from the primary vision to determine the main financial and economic drivers of this revolutionary shale oil production and how these drivers changed after 2016 when the US removed its oil-exporting ban. Design/methodology/approach – In this paper, the authors use the vector autoregressive model to assess the dynamic relationships among the Frac Count (FSCN) from the primary vision and the set of financial/macro-economic variables and how this dynamic relationship is altered with the effects of the US export ban before and after the lifting of the export ban. Findings – The empirical evidence reveals that a positive shock to New York Mercantile Exchange, Standard and Poor’s 500, rig count, West Texas Intermediate or the US ending oil stocks increase the FSCN but higher interest rates and oil production decrease the FSCN. After the US became one of the major oil producers, it removed its crude export ban in December 2015. The empirical evidence suggests that the shale oil industry gets more integrated with the financial system and becomes more efficient in its production process in the post-2016 era after the export ban was removed. Originality/value – The purpose of this paper is to use the novel data from the primary vision to determine the main financial and economic drivers of this revolutionary shale oil production and how these drivers changed after 2016 when the US removed its oil-exporting ban.
Open Access
Video copy detection using multiple visual cues and MPEG-7 descriptors
(Academic Press, 2010) Küçüktunç, O.; Baştan M.; Güdükbay, Uğur; Ulusoy, Özgür
We propose a video copy detection framework that detects copy segments by fusing the results of three different techniques: facial shot matching, activity subsequence matching, and non-facial shot matching using low-level features. In facial shot matching part, a high-level face detector identifies facial frames/shots in a video clip. Matching faces with extended body regions gives the flexibility to discriminate the same person (e.g., an anchor man or a political leader) in different events or scenes. In activity subsequence matching part, a spatio-temporal sequence matching technique is employed to match video clips/segments that are similar in terms of activity. Lastly, the non-facial shots are matched using low-level MPEG-7 descriptors and dynamic-weighted feature similarity calculation. The proposed framework is tested on the query and reference dataset of CBCD task of TRECVID 2008. Our results are compared with the results of top-8 most successful techniques submitted to this task. Promising results are obtained in terms of both effectiveness and efficiency. © 2010 Elsevier Inc. All rights reserved.
Open Access
A window-based time series feature extraction method
(Elsevier, 2017) Katircioglu-Öztürk, D.; Güvenir, H. A.; Ravens, U.; Baykal, N.
This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. © 2017 Elsevier Ltd