Extreme Value Statistics

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Infectious Disease ◽  
New York ◽  
Time Series ◽  
Real World ◽  
Scarlet Fever ◽  
Time Series Data ◽  
Extreme Value Statistics ◽  
Series Data ◽  
Disease Dynamics ◽  
Infectious Disease Dynamics

This Capstone chapter illustrates how concepts in the book come together to diagnose real-world dynamics from observed time series data. In particular, we apply NLTS to diagnose multi-strain infectious disease dynamics from weekly cases of scarlet fever, measles, and pertussis in New York during the pre-vaccine period 1924-1948.

Producers' Response to Changes in Prices and Marketing Policies: A Case Study of Sugarcane and Paddy in Eastern U.P. by S. C. Gupta and A. Majid. New York: Asia Publishing House, 1965.

1968 ◽  
Vol 8 (2) ◽  
pp. 308-309
Author(s):  
Mohammad Irshad Khan
Keyword(s):  
New York ◽  
Time Series ◽  
Time Series Data ◽  
Publishing House ◽  
Economic Incentives ◽  
The Other ◽  
Series Data ◽  
Poor Countries ◽  
Northern India

It is alleged that the agricultural output in poor countries responds very little to movements in prices and costs because of subsistence-oriented produc¬tion and self-produced inputs. The work of Gupta and Majid is concerned with the empirical verification of the responsiveness of farmers to prices and marketing policies in a backward region. The authors' analysis of the respon¬siveness of farmers to economic incentives is based on two sets of data (concern¬ing sugarcane, cash crop, and paddy, subsistence crop) collected from the district of Deoria in Eastern U.P. (Utter Pradesh) a chronically foodgrain deficit region in northern India. In one set, they have aggregate time-series data at district level and, in the other, they have obtained data from a survey of five villages selected from 170 villages around Padrauna town in Deoria.

scholarly journals ANALISIS DAMPAK KEPEMILIKAN SURAT BERHARGA OLEH BPD TERHADAP PERTUMBUHAN EKONOMI DAERAH

2018 ◽  
Vol 14 (1) ◽  
pp. 32-47
Author(s):  
Khairur dan Telisa Aulia Falian Raziqiin ◽  
Telisa Aulia Falian
Keyword(s):  
Economic Growth ◽  
New York ◽  
Time Series ◽  
Regional Development ◽  
Time Series Data ◽  
Financial Institution ◽  
Positive Influence ◽  
Regional Economic Growth ◽  
Series Data ◽  
Regional Economic

Local government-owned banks (BPD), was established in order to help accelerate the development of the area where the BPD located. The expected goals of this study are: To measure the effect of the placement of funds by BPD on regional economic growth, to measure investment lending by BPD to regional economic growth. Population was all the existing Regional Development Bank in Indonesia. Based on data from Bank Indonesia, the number of regional development banks perDesember 2013 as many as 26 banks. The type of data that will be used in this research is time series data (time series) from January 2009 until December 2013 The model that will be used in this research is the use of panel data. Results of research on Analysis of Impact of Ownership of Securities by BPD Against Regional Development, government capital spending, credit productive, ownership of securities by BPD positive effect on GDP, and significantly affect GDP, labor force have a positive influence on the GDP, but the effect was not significant workforce to GDP.Badan Pusat Statistik. Berbagai tahun. Data Realisasi APBD. Badan PusatStatistik, Jakarta. Bank Indonesia. Berbagai tahun. Laporan Publikasi Bank Umum. Bank Indonesia,Jakarta. Budiono. (2001). Ekonomi Moneter Edisi 3. Yogyakarta : BPFE Djojosubroto, Dono Iskandar. (2004). Koordinasi Kebijakan Fiskal dan Moneter di Indonesia Pasca Undang – undang Bank Indonesia 1999. Jakarta : Kompas Dornbusch, Rudiger, Stanley Fischer, Richard Startz. (2004). Makroekonomi. (Yusuf Wibisono, Roy Indra Mirazudin, terjemahan). Jakarta :MediaGlobal Edukasi. Gujarati, Damodar. (1997). Ekonometrika Dasar. (Sumarno Zein, terjemahan).Jakarta : Erlangga. Gultom, Lukdir. (2013). Tantangan Meningkatkan Efisiensi dan Efektifitas BPD sebagai Regional Champion Dalam Pengembangan Usaha Mikro, Kecil dan Menengah di Indonesia, Makalah SESPIBI Angkatan XXXI (Tidak Dipublikasikan). Bank Indonesia. Husnan, Suad. (2003). Dasar – dasar Teori Portofolio dan Analisis Sekuritas.Yogyakarta : UPP AMP YKPN. Kasmir. (2002). Dasar – Dasar Perbankan. Jakarta : PT. Raja Grafindo Persada. Kuncoro, Mudrajad. (2001) Metode Kuantitatif : Teori dan Aplikasi untuk Bisnis dan Ekonomi. Yogyakarta : AMP YKPN. Latumaerissa dan Julius R. (1999). Mengenal Aspek-aspek Operasi Bank Umum. Jakarta : Bumi Aksara. Lipsey, Richard G, et al. (1997). Pengantar Makro Ekonomi. ( Jaka Wasana danKibrandoko, terjemahan). Jakarta :Binarupa Aksara. Mankiw, Gregory. (2000). Macroeconomics Theory. New York : Worth PublisherInc. Nachrowi, Nachrowi D., Hardius Usman. (2006). Pendekatan Populer dan Praktis EKONOMETRIKA untuk Analisis Ekonomi dan Keuangan.Jakarta : Lembaga Penerbit FEUI. Rahmany, A. Fuad. (2004). Era Baru Kebijakan Fiskal : Pemikiran, Konsep dan Implementasi. Jakarta : Penerbit Buku Kompas, hal. 445 – 462. Rivai, Veithzal, Andria Permata Veithzal, Ferry N. Idroes. (2007). Bank and Financial Institution Management : Conventional & Sharia System, Jakarta : RajaGrafindo Persada. Sunarsip. (2008). Relasi Bank Pembangunan Daerah dan Perekonomian Daerah, dimuat dalam Republika, Rabu, 9 Januari 2008. Rubrik Pareto hal.16 Sunarsip. (2011). Transformasi BPD. Dimuat Infobank Edisi Januari 2011. Republik Indonesia, Kementrian Keuangan (2010), Potensi Bank Pembangunan Daerah Sebagai Pendiri Dana Pensiun Lembaga Keuangan,Tim Studi Potensi Bank Pembangunan Daerah Sebagai Pendiri Dana Pensiun. Jakarta.Waluyanto, Rahmat. (2004). Era Baru Kebijakan Fiskal : Pemikiran, Konsep dan Implementasi. Jakarta : Penerbit Buku Kompas, hal. 463 – 508. Wuryandari, Gantiah. (2013). Mengusung Bank Pembangunan Daerah (BPD) Sebagai Bank Fokus Sektor Strategis Dalam Mendukung Pembangunan Nasional, Makalah SESPIBI Angkatan XXXI (Tidak Dipublikasikan). Bank Indonesia.

scholarly journals Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 4683-4690 ◽  
Author(s):  
Shuheng Li ◽  
Dezhi Hong ◽  
Hongning Wang
Keyword(s):  
Time Series ◽  
Demand Response ◽  
Real World ◽  
Time Series Data ◽  
Metric Learning ◽  
Fault Detection And Diagnosis ◽  
Series Data ◽  
Smart Building ◽  
Deep Metric Learning ◽  
Detection And Diagnosis

Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution.

Sensors to Events: Semantic Modeling and Recognition of Events from Data Streams

2016 ◽  
Vol 10 (04) ◽  
pp. 461-501 ◽  
Author(s):  
Om Prasad Patri ◽  
Anand V. Panangadan ◽  
Vikrambhai S. Sorathia ◽  
Viktor K. Prasanna
Keyword(s):  
Time Series ◽  
Real World ◽  
Data Streams ◽  
Time Series Data ◽  
Semantic Representation ◽  
Expressive Power ◽  
Sensor Data ◽  
Series Data ◽  
Formal Approach ◽  
Semantic Computing

Detecting and responding to real-world events is an integral part of any enterprise or organization, but Semantic Computing has been largely underutilized for complex event processing (CEP) applications. A primary reason for this gap is the difference in the level of abstraction between the high-level semantic models for events and the low-level raw data values received from sensor data streams. In this work, we investigate the need for Semantic Computing in various aspects of CEP, and intend to bridge this gap by utilizing recent advances in time series analytics and machine learning. We build upon the Process-oriented Event Model, which provides a formal approach to model real-world objects and events, and specifies the process of moving from sensors to events. We extend this model to facilitate Semantic Computing and time series data mining directly over the sensor data, which provides the advantage of automatically learning the required background knowledge without domain expertise. We illustrate the expressive power of our model in case studies from diverse applications, with particular emphasis on non-intrusive load monitoring in smart energy grids. We also demonstrate that this powerful semantic representation is still highly accurate and performs at par with existing approaches for event detection and classification.

scholarly journals Modelling the Working Week for Multi-Step Forecasting using Gaussian Process Regression

2017 ◽  
Author(s):  
Pasan Karunaratne ◽  
Masud Moshtaghi ◽  
Shanika Karunasekera ◽  
Aaron Harwood ◽  
Trevor Cohn
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Real World ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Series Data ◽  
Combination Methods ◽  
The University ◽  
The City ◽  
Source Of Information

In time-series forecasting, regression is a popular method, with Gaussian Process Regression widely held to be the state of the art. The versatility of Gaussian Processes has led to them being used in many varied application domains. However, though many real-world applications involve data which follows a working-week structure, where weekends exhibit substantially different behavior to weekdays, methods for explicit modelling of working-week effects in Gaussian Process Regression models have not been proposed. Not explicitly modelling the working week fails to incorporate a significant source of information which can be invaluable in forecasting scenarios. In this work we provide novel kernel-combination methods to explicitly model working-week effects in time-series data for more accurate predictions using Gaussian Process Regression. Further, we demonstrate that prediction accuracy can be improved by constraining the non-convex optimization process of finding optimal hyperparameter values. We validate the effectiveness of our methods by performing multi-step prediction on two real-world publicly available time-series datasets - one relating to electricity Smart Meter data of the University of Melbourne, and the other relating to the counts of pedestrians in the City of Melbourne.

scholarly journals A sub-exponential branching process to study early epidemic dynamics with application to Ebola

2019 ◽  
Author(s):  
Alexander E. Zarebski ◽  
Robert Moss ◽  
James M. McCaw
Keyword(s):  
Infectious Disease ◽  
Time Series ◽  
West Africa ◽  
Real Time ◽  
Exponential Growth ◽  
Branching Process ◽  
Ebola Virus ◽  
Time Series Data ◽  
Contact Tracing ◽  
Series Data

AbstractExponential growth is a mathematically convenient model for the early stages of an outbreak of an infectious disease. However, for many pathogens (such as Ebola virus) the initial rate of transmission may be sub-exponential, even before transmission is affected by depletion of susceptible individuals.We present a stochastic multi-scale model capable of representing sub-exponential transmission: an in-homogeneous branching process extending the generalised growth model. To validate the model, we fit it to data from the Ebola epidemic in West Africa (2014–2016). We demonstrate how a branching process can be fit to both time series of confirmed cases and chains of infection derived from contact tracing. Our estimates of the parameters suggest transmission of Ebola virus was sub-exponential during this epidemic. Both the time series data and the chains of infections lead to consistent parameter estimates. Differences in the data sets meant consistent estimates were not a foregone conclusion. Finally, we use a simulation study to investigate the properties of our methodology. In particular, we examine the extent to which the estimates obtained from time series data and those obtained from chains of infection data agree.Our method, based on a simple branching process, is well suited to real-time analysis of data collected during contact tracing. Identifying the characteristic early growth dynamics (exponential or sub-exponential), including an estimate of uncertainty, during the first phase of an epidemic should prove a useful tool for preliminary outbreak investigations.Author SummaryEpidemic forecasts have the potential to support public health decision making in outbreak scenarios for diseases such as Ebola and influenza. In particular, reliable predictions of future incidence data may guide surveillance and intervention responses. Existing methods for producing forecasts, based upon mechanistic transmission models, often make an implicit assumption that growth is exponential, at least while susceptible depletion remains negligible. However, empirical studies suggest that many infectious disease outbreaks display sub-exponential growth early in the epidemic. Here we introduce a mechanistic model of early epidemic growth that allows for sub-exponential growth in incidence. We demonstrate how the model can be applied to the types of data that are typically available in (near) real-time, including time series data on incidence as well as individual-level case series and chains of transmission data. We apply our methods to publically available data from the 2014–2016 West Africa Ebola epidemic and demonstrate that early epidemic growth was sub-exponential. We also investigate the statistical properties of our model through a simulation re-estimation study to identify it performance characteristics and avenues for further methodological research.

scholarly journals Adaptive Synapse Arrangement in Cortical Learning Algorithm

2021 ◽  
Vol 25 (4) ◽  
pp. 450-466
Author(s):  
Takeru Aoki ◽  
◽  
Keiki Takadama ◽  
Hiroyuki Sato
Keyword(s):  
New York ◽  
Time Series ◽  
Prediction Accuracy ◽  
Input Data ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Prediction Method ◽  
Series Data ◽  
Load Prediction ◽  
Data Prediction

The cortical learning algorithm (CLA) is a time-series data prediction method that is designed based on the human neocortex. The CLA has multiple columns that are associated with the input data bits by synapses. The input data is then converted into an internal column representation based on the synapse relation. Because the synapse relation between the columns and input data bits is fixed during the entire prediction process in the conventional CLA, it cannot adapt to input data biases. Consequently, columns not used for internal representations arise, resulting in a low prediction accuracy in the conventional CLA. To improve the prediction accuracy of the CLA, we propose a CLA that self-adaptively arranges the column synapses according to the input data tendencies and verify its effectiveness with several artificial time-series data and real-world electricity load prediction data from New York City. Experimental results show that the proposed CLA achieves higher prediction accuracy than the conventional CLA and LSTMs with different network optimization algorithms by arranging column synapses according to the input data tendency.

Data Preprocessing

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Signal Processing ◽  
Real World ◽  
Null Hypothesis ◽  
Time Series Data ◽  
Nonlinear Behavior ◽  
Surrogate Data ◽  
Series Data ◽  
Linear Behavior

Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test this null hypothesis using surrogate data testing. The combination of a noticeably regular shadow attractor, along with strong statistical rejection of fortuitous regularity, increases the probability that observed data are generated by deterministic real-world dynamics.

scholarly journals Unsupervised Pre-training of a Deep LSTM-based Stacked Autoencoder for Multivariate Time Series Forecasting Problems

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Alaa Sagheer ◽  
Mostafa Kotb
Keyword(s):  
Time Series ◽  
Case Studies ◽  
Real World ◽  
Time Series Data ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Series Data ◽  
Recurrent Networks ◽  
Stacked Autoencoder ◽  
Real World Datasets

AbstractCurrently, most real-world time series datasets are multivariate and are rich in dynamical information of the underlying system. Such datasets are attracting much attention; therefore, the need for accurate modelling of such high-dimensional datasets is increasing. Recently, the deep architecture of the recurrent neural network (RNN) and its variant long short-term memory (LSTM) have been proven to be more accurate than traditional statistical methods in modelling time series data. Despite the reported advantages of the deep LSTM model, its performance in modelling multivariate time series (MTS) data has not been satisfactory, particularly when attempting to process highly non-linear and long-interval MTS datasets. The reason is that the supervised learning approach initializes the neurons randomly in such recurrent networks, disabling the neurons that ultimately must properly learn the latent features of the correlated variables included in the MTS dataset. In this paper, we propose a pre-trained LSTM-based stacked autoencoder (LSTM-SAE) approach in an unsupervised learning fashion to replace the random weight initialization strategy adopted in deep LSTM recurrent networks. For evaluation purposes, two different case studies that include real-world datasets are investigated, where the performance of the proposed approach compares favourably with the deep LSTM approach. In addition, the proposed approach outperforms several reference models investigating the same case studies. Overall, the experimental results clearly show that the unsupervised pre-training approach improves the performance of deep LSTM and leads to better and faster convergence than other models.

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