scholarly journals Detecting Outbreaks in Time-Series Data with RecentMax

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Dave Carter ◽  
Joel D. Martin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Influenza Like Illness ◽  
Transmissible Disease ◽  
Better Than

The RecentMax algorithm seeks to detect typical outbreaks of transmissible disease (particularly influenza-like illness) in time-series data better than existing algorithms like CDC EARS C1/C2/C3.

scholarly journals Is Islamic Bank More Stable Than Conventional Bank? Evidence From Islamic Rural Banks in Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 294
Author(s):  
Agus Widarjono ◽  
M. B. Hendrie Anto ◽  
Faaza Fakhrunnas
Keyword(s):  
Time Series ◽  
Financial Stability ◽  
Time Series Data ◽  
Series Data ◽  
Z Score ◽  
Profit Rate ◽  
Return On Assets ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Better Than

This study investigates whether Islamic rural banks perform better than conventional rural banks as their competitor in Indonesia. To measure Islamic rural banks' financial performance, we apply financial stability using Z-score and profitability using the return on assets. We use monthly time series data from January 2009 to December 2018. The dynamic regression of the Autoregressive Distributed Lag (ARDL) model is then employed. The results report that the Z-Score of Islamic rural banks is higher than the Z-Score of conventional rural banks. This finding shows that Islamic rural banks are less risky than conventional rural banks. However, the Islamic rural banks' financial stability is very vulnerable to changes in equity, output, and inflation than conventional rural banks. Although the Islamic rural banks' profit rate is lower compared to conventional rural banks, it is considered more stable. The profit of Islamic rural banks is affected by size, equity, domestic output, and inflation.

scholarly journals Hybrid Models for Adaptive Allocation of Electricity for Households

2019 ◽  
Vol 9 (2S) ◽  
pp. 369-376
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Active Power ◽  
Sensor Data ◽  
Series Data ◽  
Adaptive Allocation ◽  
Forecasting Methods ◽  
Better Than

In this paper, we analyze, model, predict and cluster Global Active Power, i.e., a time series data obtained at one minute intervals from electricity sensors of a household. We analyze changes in seasonality and trends to model the data. We then compare various forecasting methods such as SARIMA and LSTM to forecast sensor data for the household and combine them to achieve a hybrid model that captures nonlinear variations better than either SARIMA or LSTM used in isolation. Finally, we cluster slices of time series data effectively using a novel clustering algorithm that is a combination of density-based and centroid-based approaches, to discover relevant subtle clusters from sensor data. Our experiments have yielded meaningful insights from the data at both a micro, day-to-day granularity, as well as a macro, weekly to monthly granularity.

scholarly journals Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Toby Kenney ◽  
Junqiu Gao ◽  
Hong Gu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Stable State ◽  
Mean Reversion ◽  
Series Data ◽  
Optimal Sampling ◽  
Healthy Individuals ◽  
Temporal Continuity ◽  
Better Than

Abstract Background The vast majority of microbiome research so far has focused on the structure of the microbiome at a single time-point. There have been several studies that measure the microbiome from a particular environment over time. A few models have been developed by extending time series models to accomodate specific features in microbiome data to address questions of stability and interactions of the microbime time series. Most research has observed the stability and mean reversion for some microbiomes. However, little has been done to study the mean reversion rates of these stable microbes and how sampling frequencies are related to such conclusions. In this paper, we begin to rectify this situation. We analyse two widely studied microbial time series data sets on four healthy individuals. We choose to study healthy individuals because we are interested in the baseline temporal dynamics of the microbiome. Results For this analysis, we focus on the temporal dynamics of individual genera, absorbing all interactions in a stochastic term. We use a simple stochastic differential equation model to assess the following three questions. (1) Does the microbiome exhibit temporal continuity? (2) Does the microbiome have a stable state? (3) To better understand the temporal dynamics, how frequently should data be sampled in future studies? We find that a simple Ornstein–Uhlenbeck model which incorporates both temporal continuity and reversion to a stable state fits the data for almost every genus better than a Brownian motion model that contains only temporal continuity. The Ornstein–Uhlenbeck model also fits the data better than modelling separate time points as independent. Under the Ornstein–Uhlenbeck model, we calculate the variance of the estimated mean reversion rate (the speed with which each genus returns to its stable state). Based on this calculation, we are able to determine the optimal sample schemes for studying temporal dynamics. Conclusions There is evidence of temporal continuity for most genera; there is clear evidence of a stable state; and the optimal sampling frequency for studying temporal dynamics is in the range of one sample every 0.8–3.2 days.

scholarly journals ANALISIS ARIMA DAN WAVELET UNTUK PERAMALAN HARGA CABAI MERAH BESAR DI JAWA TENGAH

2020 ◽  
Vol 9 (3) ◽  
pp. 247-262
Author(s):  
Chrisentia Widya Ardianti ◽  
Rukun Santoso ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Parametric Method ◽  
Haar Wavelet ◽  
Time Span ◽  
Series Data ◽  
Central Java ◽  
The One ◽  
Better Than ◽  
Analysis Of Time Series

Time series is a type of data collected according to the sequence of times in a certain time span. Time series data can be used as a predictor of future conditions. Analysis of time series data, one of the ARIMA units, is a parametric method that requires an assumption to get valid results. Data stationarity is one of the factors that must be fulfilled. Wavelet is a non-parametric method that is able to represent time and frequency information simultaneously, so that it can analyze non-stationary data. This research presents forecasting the price of red chili in Central Java using ARIMA and wavelet with the approach of the Multiscale Autoregressive (MAR) model. The best model is the one with the smallest MSE value. The results showed that the ARIMA(0,1,1) model was said to be the best model with MSE = 2252142. However, because the assumption of normality is not fulfilled, an alternative process is done with wavelet. Wavelet approach results show that the MAR model Haar filter level (j) = 4 with MSE = 2175906 is better than Daubechies 4 filter 4 level (j) = 1 with MSE = 3999669. Therefore, the Haar wavelet is considered better in the time series analysis. Keyword : ARIMA, wavelet, MAR, forecasting, MSE

scholarly journals Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes.

2020 ◽  
Author(s):  
Toby Kenney ◽  
Junqiu Gao ◽  
Hong Gu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Stable State ◽  
Mean Reversion ◽  
Series Data ◽  
Optimal Sampling ◽  
Healthy Individuals ◽  
Temporal Continuity ◽  
Better Than

Abstract Background: The vast majority of microbiome research so far has focused on the structure of the microbiome at a single time-point. There have been several studies that measure the microbiome from a particular environment over time. A few models have been developed by extending time series models to accomodate specific features in microbiome data to address questions of stability and interactions of the microbime time series. Most research has observed the stability and mean reversion for some microbiomes. However, little has been done to study the mean reversion rates of these stable microbes and how sampling frequencies are related to such conclusions. In this paper, we begin to rectify this situation. We analyse two widely studied microbial time series data sets on four healthy individuals. We choose to study healthy individuals because we are interested in the baseline temporal dynamics of the microbiome. Results: For this analysis, we focus on the temporal dynamics of individual genera, absorbing all interactions in a stochastic term. We use a simple stochastic differential equation models to assess the following three questions. (1) Does the microbiome exhibit temporal continuity? (2) Does the microbiome have a stable state? (3) To better understand the temporal dynamics, how frequently should data be sampled in future studies? We find that a simple Ornstein-Uhlenbeck model which incorporates both temporal continuity and reversion to a stable state fits the data for almost every genus better than a Brownian motion model that contains only temporal continuity. The Ornstein-Uhlenbeck model also fits the data better than modelling separate time points as independent. Under the Ornstein-Uhlenbeck model, we calculate the variance of the estimated mean reversion rate (the speed with which each genus returns to its stable state). Based on this calculation, we are able to determine the optimal sample schemes for studying temporal dynamics. Conclusions: There is evidence of temporal continuity for most genera; there is clear evidence of a stable state; and the optimal sampling frequency for studying temporal dynamics is in the range of one sample every 0.8–3.2 days.

scholarly journals Application of OU processes to modelling temporal dynamics of the human microbiome, and calculating optimal sampling schemes.

2020 ◽  
Author(s):  
Toby Kenney ◽  
Junqiu Gao ◽  
Hong Gu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Temporal Dynamics ◽  
Stable State ◽  
Mean Reversion ◽  
Series Data ◽  
Optimal Sampling ◽  
Healthy Individuals ◽  
Temporal Continuity ◽  
Better Than

Abstract Background: The vast majority of microbiome research so far has focused on the structure of the microbiome at a single time-point. There have been several studies that measure the microbiome from a particular environment over time. A few models have been developed by extending time series models to accomodate specific features in microbiome data to address questions of stability and interactions of the microbime time series. Most research has observed the stability and mean reversion for some microbiomes. However, little has been done to study the mean reversion rates of these stable microbes and how sampling frequencies are related to such conclusions. In this paper, we begin to rectify this situation. We analyse two widely studied microbial time series data sets on four healthy individuals. We choose to study healthy individuals because we are interested in the baseline temporal dynamics of the microbiome. Results: For this analysis, we focus on the temporal dynamics of individual genera, absorbing all interactions in a stochastic term. We use a simple stochastic differential equation models to assess the following three questions. (1) Does the microbiome exhibit temporal continuity? (2) Does the microbiome have a stable state? (3) To better understand the temporal dynamics, how frequently should data be sampled in future studies? We find that a simple Ornstein-Uhlenbeck model which incorporates both temporal continuity and reversion to a stable state fits the data for almost every genus better than a Brownian motion model that contains only temporal continuity. The Ornstein-Uhlenbeck model also fits the data better than modelling separate time points as independent. Under the Ornstein-Uhlenbeck model, we calculate the variance of the estimated mean reversion rate (the speed with which each genus returns to its stable state). Based on this calculation, we are able to determine the optimal sample schemes for studying temporal dynamics. Conclusions: There is evidence of temporal continuity for most genera; there is clear evidence of a stable state; and the optimal sampling frequency for studying temporal dynamics is in the range of one sample every 0.8–3.2 days.

scholarly journals A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment

2020 ◽  
Vol 9 (4) ◽  
pp. 53
Author(s):  
Basma Mahdy ◽  
Hazem Abbas ◽  
Hossam Hassanein ◽  
Aboelmagd Noureldin ◽  
Hatem Abou-zeid
Keyword(s):  
Time Series ◽  
Mobile Networks ◽  
Time Series Data ◽  
Mobile Network ◽  
Base Station ◽  
Optimization Techniques ◽  
Base Stations ◽  
Traffic Load ◽  
Series Data ◽  
Better Than

Mobile network traffic is increasing in an unprecedented manner, resulting in growing demand from network operators to deploy more base stations able to serve more devices while maintaining a satisfactory level of service quality. Base stations are considered the leading energy consumer in network infrastructure; consequently, increasing the number of base stations will increase power consumption. By predicting the traffic load on base stations, network optimization techniques can be applied to decrease energy consumption. This research explores different machine learning and statistical methods capable of predicting traffic load on base stations. These methods are examined on a public dataset that provides records of traffic loads of several base stations over the span of one week. Because of the limited number of records in the dataset for each base station, different base stations are grouped while building the prediction model. Due to the different behavior of the base stations, forecasting the traffic load of multiple base stations together becomes challenging. The proposed solution involves clustering the base stations according to their behavior and forecasting the load on the base stations in each cluster individually. Clustering the time series data according to their behavior mitigates the dissimilar behavior problem of the time series when they are trained together. Our findings demonstrate that predictions based on deep recurrent neural networks perform better than other forecasting techniques.

scholarly journals ALTERNATIF PENGGEROMBOLAN DATA DERET WAKTU DENGAN KONDISI TERDAPAT DATA KOSONG

2018 ◽  
Vol 2 (1) ◽  
pp. 13-22
Author(s):  
Yusma Yanti ◽  
Septian Rahardiantoro
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Data ◽  
Series Data ◽  
Correlation Matrices ◽  
Alternative Method ◽  
Data Value ◽  
Ratio Data ◽  
Different Characteristics ◽  
Better Than

Panel data describes a condition in which there are many observations with each observation observed periodically over a period of time. The observation clustering context based on this data is known as Clustering of Time Series Data. Many methods are developed based on fluctuating time series data conditions. However, missing data causes problems in this analysis. Missing data is the unavailability of data value on an observation because there is no information related to it. This study attempts to provide an alternative method of clustering observations on data with time series containing missing data by utilizing correlation matrices converted into Euclid distance matrices which are subsequently applied by the hierarchical clustering method. The simulation process was done to see the goodness of alternative method with common method used in data with 0%, 10%, 20% and 40% missing data condition. The result was obtained that the accuracy of the observation bundling on the proposed alternative method is always better than the commonly used method. Furthermore, the implementation was done on the annual gini ratio data of each province in Indonesia in 2007 to 2017 which contained missing data in North Kalimantan Province. There were 2 clusters of province with different characteristics.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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