A Binary Time Series Model of LTE Scheduling for Machine Learning Prediction

2016 ◽  
Author(s):  
Jonathan Ah Sue ◽  
Ralph Hasholzner ◽  
Johannes Brendel ◽  
Martin Kleinsteuber ◽  
Juergen Teich
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Model ◽  
Binary Time Series

scholarly journals Analysis of psychometric data using statistical and machine learning methods

2021 ◽  
Author(s):  
Krishnapriya Subramanian
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Well Being ◽  
Time Series Model ◽  
Learning Methods ◽  
Logistics Regression ◽  
Machine Learning Methods ◽  
Psychometric Data ◽  
Simulation Results

The objective of this thesis is to analyse the psychometric data using statistical and machine learning methods. Psychological data are analysed to predict illness and injury of athletes. Regression technique, one of the statistical processes for estimating the relationship among variables is used as basis of this thesis. We apply the linear regression, time series and logistics regression to predict illness and well-being. Our linear regression simulation results are mainly used, to understand the data well. By reviewing the results of linear regression, time series model is developed which predicts sickness one day ahead. The predicted values of this time series model are continuous. However, logistic regression can be used, to provide a probabilistic approach to predict the future levels as a categorical value. Hence we have developed a binomial logistics regression model, when observation variable is the type of dichotomous. Our simulation results show that this prediction model performs well. Our empirical studies also show that our method can act as early warning system for athletes.

scholarly journals Persistence Analysis and Prediction of Low-Visibility Events at Valladolid Airport, Spain

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1045
Author(s):  
Sara Cornejo-Bueno ◽  
David Casillas-Pérez ◽  
Laura Cornejo-Bueno ◽  
Mihaela I. Chidean ◽  
Antonio J. Caamaño ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Markov Chain ◽  
Large Scale ◽  
Machine Learning Techniques ◽  
Fluctuation Analysis ◽  
Short Term ◽  
Binary Time Series ◽  
Low Visibility ◽  
Short Term Prediction

This work presents an analysis of low-visibility event persistence and prediction at Villanubla Airport (Valladolid, Spain), considering Runway Visual Range (RVR) time series in winter. The analysis covers long- and short-term persistence and prediction of the series, with different approaches. In the case of long-term analysis, a Detrended Fluctuation Analysis (DFA) approach is applied in order to estimate large-scale RVR time series similarities. The short-term persistence analysis of low-visibility events is evaluated by means of a Markov chain analysis of the binary time series associated with low-visibility events. We finally discuss an hourly short-term prediction of low-visibility events, using different approaches, some of them coming from the persistence analysis through Markov chain models, and others based on Machine Learning (ML) techniques. We show that a Mixture of Experts approach involving persistence-based methods and Machine Learning techniques provides the best results in this prediction problem.

scholarly journals Analysis of psychometric data using statistical and machine learning methods

2021 ◽  
Author(s):  
Krishnapriya Subramanian
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Linear Regression ◽  
Well Being ◽  
Time Series Model ◽  
Learning Methods ◽  
Logistics Regression ◽  
Machine Learning Methods ◽  
Psychometric Data ◽  
Simulation Results

The objective of this thesis is to analyse the psychometric data using statistical and machine learning methods. Psychological data are analysed to predict illness and injury of athletes. Regression technique, one of the statistical processes for estimating the relationship among variables is used as basis of this thesis. We apply the linear regression, time series and logistics regression to predict illness and well-being. Our linear regression simulation results are mainly used, to understand the data well. By reviewing the results of linear regression, time series model is developed which predicts sickness one day ahead. The predicted values of this time series model are continuous. However, logistic regression can be used, to provide a probabilistic approach to predict the future levels as a categorical value. Hence we have developed a binomial logistics regression model, when observation variable is the type of dichotomous. Our simulation results show that this prediction model performs well. Our empirical studies also show that our method can act as early warning system for athletes.

scholarly journals Prediksi Penjualan Supermarket Menggunakan Pendekatan Deep Learning

2021 ◽  
Vol 7 (1) ◽  
pp. 160
Author(s):  
Marchel Thimoty Tombeng ◽  
Zalfie Ardian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Short Term Memory ◽  
Time Series Model ◽  
Time Machine ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Berdasarkan data transaksi tahun 2014 sampai 2016 dari salah satu supermarket yang ada di Taiwan, penulis menghasilkan analisa model prediksi dengan menguji data menggunakan metode Deep Learning. Beberapa faktor yang berpengaruh telah di dipelajari dan berguna untuk input prediksi, antara lain keadaan cuacu, diskon, hari raya, dan lain sebagainya. Motivasi utama dari penelitian yang penulis lakukan adalah menggunakan teknologi yang berhubungan dengan eksplorasi data untuk memprediksikan penjualan dari produk-produk dan waktu berkunjung pelangan dalam industry retail, untuk mencari grup target yang tepat dan korelasi produk yang tinggi. Pada akhirnya penulis menciptakan sistem keputusan produk yang berisi analisa visual dan tindakan saran untuk manajer produk pemasaran serta pemangku kepentingan dalam pemasaran produk. Dengan adanya hasil prediksi ini, diharapkan dapat menbantu manajer atau pemangku kepentingan lainnya untuk dapat memasarkan serta menjual produk secara tepat sehingga dapat menghasilkan keuntungan yang banyak dengan menggunkan analisa prediksi yang kami buat. LSTM merupakan model yang sering dipakai dalam Recursive Neural Network (RNN), dan pada dasarnya berfungsi untuk memecahkan masalah dari Time Series. Model Deep Learning yang penulis gunakan adalah Long Short Term Memory (LSTM), dimana model ini menyediakan analisa dan prediksi dari serangkaian data. Sebagai contoh, pada saat akhir pekan pengunjungnya melonjat, maka time machine learning ini akan menambahkan pengartian dari nilai parameter akhir pekan dan nilai ouputnya memiliki korelasi yang kuat.Kata kunci—Predictions, Time Series, LSTM, RNN, Deep Learning

Wind power forecasting based on time series model using deep machine learning algorithms

2021 ◽  
Author(s):  
V. Chandran ◽  
Chandrashekhar K. Patil ◽  
Anto Merline Manoharan ◽  
Aritra Ghosh ◽  
M.G. Sumithra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Wind Power ◽  
Learning Algorithms ◽  
Time Series Model ◽  
Machine Learning Algorithms ◽  
Wind Power Forecasting ◽  
Power Forecasting

About One Approach to Defuzzifikation of Outputs of Fuzzy Time Series Model

2011 ◽  
Vol 3 (9) ◽  
pp. 562-566
Author(s):  
Ramin Rzayev ◽  
◽  
Musa Agamaliyev ◽  
Nijat Askerov
Keyword(s):  
Time Series ◽  
Time Series Model ◽  
Fuzzy Time Series
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