Identifying Seasonality in Time Series by Applying Fast Fourier Transform

Musbah, Hmeda; El-Hawary, M.E.; Aly, Hamed H.

doi:10.1109/epec47565.2019.9074776

Cited by 14 publications

(4 citation statements)

References 3 publications

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“…We used the Fast Fourier Transform (FFT) to detect the presence of any seasonality in our time series data. The FFT allows us to transform a function of time and signal into a function of frequency and power (Musbah et al, 2019 ). This depicts the frequencies that make up the data in the original domain (time) and their relative strengths, as illustrated in Figure 4 .…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy

Singh

Chakraborty²,

He³

et al. 2022

Front. Psychol.

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As the population ages, the number of older adults experiencing mild cognitive impairment (MCI), Alzheimer's disease, and other forms of dementia will increase dramatically over the next few decades. Unfortunately, cognitive changes associated with these conditions threaten independence and quality of life. To address this, researchers have developed promising cognitive training interventions to help prevent or reverse cognitive decline and cognitive impairment. However, the promise of these interventions will not be realized unless older adults regularly engage with them over the long term, and like many health behaviors, adherence to cognitive training interventions can often be poor. To maximize training benefits, it would be useful to be able to predict when adherence lapses for each individual, so that support systems can be personalized to bolster adherence and intervention engagement at optimal time points. The current research uses data from a technology-based cognitive intervention study to recognize patterns in participants' adherence levels and predict their future adherence to the training program. We leveraged the feature learning capabilities of deep neural networks to predict patterns of adherence for a given participant, based on their past behavior. A separate, personalized model was trained for each participant to capture individualistic features of adherence. We posed the adherence prediction as a binary classification problem and exploited multivariate time series analysis using an adaptive window size for model training. Further, data augmentation techniques were used to overcome the challenge of limited training data and enhance the size of the dataset. To the best of our knowledge, this is the first research effort to use advanced machine learning techniques to predict older adults' daily adherence to cognitive training programs. Experimental evaluations corroborated the promise and potential of deep learning models for adherence prediction, which furnished highest mean F-scores of 75.5, 75.5, and 74.6% for the Convolution Neural Network (CNN), Long Short-Term Memory (LSTM) network, and CNN-LSTM models respectively.

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Section: Methodsmentioning

confidence: 99%

Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy

Singh

Chakraborty²,

He³

et al. 2022

Front. Psychol.

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“…Fast Fourier Transform. The Fast Fourier Transform (FFT) decomposes the time series into its frequency components [71,72], which are then used to predict future values of the time series. It is particularly suitable for highly seasonal data.…”

Section: Baselinesmentioning

confidence: 99%

Predicting the Price of Bitcoin Using Sentiment-Enriched Time Series Forecasting

Frohmann,

Karner,

Khudoyan

et al. 2023

BDCC

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Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the future price of Bitcoin, which is currently the most popular cryptocurrency. More precisely, we propose a hybrid approach, combining time series forecasting and sentiment prediction from microblogs, to predict the intraday price of Bitcoin. Moreover, in addition to standard sentiment analysis methods, we are the first to employ a fine-tuned BERT model for this task. We also introduce a novel weighting scheme in which the weight of the sentiment of each tweet depends on the number of its creator’s followers. For evaluation, we consider periods with strongly varying ranges of Bitcoin prices. This enables us to assess the models w.r.t. robustness and generalization to varied market conditions. Our experiments demonstrate that BERT-based sentiment analysis and the proposed weighting scheme improve upon previous methods. Specifically, our hybrid models that use linear regression as the underlying forecasting algorithm perform best in terms of the mean absolute error (MAE of 2.67) and root mean squared error (RMSE of 3.28). However, more complicated models, particularly long short-term memory networks and temporal convolutional networks, tend to have generalization and overfitting issues, resulting in considerably higher MAE and RMSE scores.

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“…This period can be expressed in hours, days, weeks. The fast Fourier transform was used [3] and some results of this approach are visible in Figs. 1 and 2, where the frequency axis (Fig.…”

Section: Determining the Degree Of The Data Repeatability In Metricsmentioning

confidence: 99%

The Support System for Anomaly Detection with Application in Mainframe Management Process

Strzałka¹,

Gerka²,

Kuraś³

et al. 2021

Frontiers in Artificial Intelligence and Applications

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The process of mainframe machines managing and administration requires not only specialized expert knowledge based on many years of experience but also on appropriate tools provided by a machine performance management system, e.g. the Resource Measurement Facility (RMF). The aim of this paper is to show some preliminary results of Z-RAYS system construction that is built basing on machine learning (ML) techniques. It allows automatic detection of anomalies and generation of early warnings about some errors that can appear in the mainframe to support mainframe management process. Presented results are based on extensive simulations that were done basing on the IBM emulator. We focus on determining the degree of the metrics variability, the degree of the data repeatability in metrics, some approaches in metrics anomaly detection and solutions for event correlation detection in metrics.

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Identifying Seasonality in Time Series by Applying Fast Fourier Transform

Cited by 14 publications

References 3 publications

Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy

Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy

Predicting the Price of Bitcoin Using Sentiment-Enriched Time Series Forecasting

The Support System for Anomaly Detection with Application in Mainframe Management Process

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