scholarly journals Systemic lupus Erythematosus and geomagnetic disturbances: a time series analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
George Stojan ◽  
Flavia Giammarino ◽  
Michelle Petri
Keyword(s):  
Time Series ◽  
Disease Activity ◽  
Sunspot Number ◽  
Geomagnetic Activity ◽  
High Energy ◽  
Bonferroni Correction ◽  
Geomagnetic Disturbances ◽  
Short Term ◽  
Ap Index

Abstract Background To examine the influence of solar cycle and geomagnetic effects on SLE disease activity. Methods The data used for the analysis consisted of 327 observations of 27-day Physician Global Assessment (PGA) averages from January 1996 to February 2020. The considered geomagnetic indices were the AP index (a daily average level for geomagnetic activity), sunspot number index R (measure of the area of solar surface covered by spots), the F10.7 index (measure of the noise level generated by the sun at a wavelength of 10.7 cm at the earth’s orbit), the AU index (upper auroral electrojet index), and high energy (> 60 Mev) proton flux events. Geomagnetic data were obtained from the Goddard Space Flight Center Space Physics Data Facility. A time series decomposition of the PGA averages was performed as the first step. The linear relationships between the PGA and the geomagnetic indices were examined using parametric statistical methods such as Pearson correlation and linear regression, while the nonlinear relationships were examined using nonparametric statistical methods such as Spearman’s rho and Kernel regression. Results After time series deconstruction of PGA averages, the seasonality explained a significant fraction of the variance of the time series (R2 = 38.7%) with one cycle completed every 16 years. The analysis of the short-term (27-day) relationships indicated that increases in geomagnetic activity Ap index (p < 0.1) and high energy proton fluxes (> 60 Mev) (p < 0.05) were associated with decreases in SLE disease activity, while increases in the sunspot number index R anticipated decreases in the SLE disease activity expressed as PGA (p < 0.05). The short-term correlations became statistically insignificant after adjusting for multiple comparisons using Bonferroni correction. The analysis of the long-term (297 day) relationships indicated stronger negative association between changes in the PGA and changes in the sunspot number index R (p < 0.01), AP index (p < 0.01), and the F10.7 index (p < 0.01). The long-term correlations remained statistically significant after adjusting for multiple comparisons using Bonferroni correction. Conclusion The seasonality of the PGA averages (one cycle every 16 years) explains a significant fraction of the variance of the time series. Geomagnetic disturbances, including the level of geomagnetic activity, sunspot numbers, and high proton flux events may influence SLE disease activity. Studies of other geographic locales are needed to validate these findings.

scholarly journals Comparison of Geomagnetic Indices During Even and Odd Solar Cycles SC17 – SC24: Signatures of Gnevyshev Gap in Geomagnetic Activity

Solar Physics ◽  
2021 ◽  
Vol 296 (1) ◽  
Author(s):  
Jouni Takalo
Keyword(s):  
Time Series ◽  
Geomagnetic Activity ◽  
Sunspot Group ◽  
Solar Cycles ◽  
Geomagnetic Disturbances ◽  
Similar Length ◽  
Dst Index ◽  
Small Decline ◽  
Ap Index ◽  
Odd Cycles

AbstractWe show that the time series of sunspot group areas has a gap, the so-called Gnevyshev gap (GG), between ascending and descending phases of the cycle and especially so for the even-numbered cycles. For the odd cycles this gap is less obvious, and is only a small decline after the maximum of the cycle. We resample the cycles to have the same length of 3945 days (about 10.8 years), and show that the decline is between 1445 – 1567 days after the start of the cycle for the even cycles, and extending sometimes until 1725 days from the start of the cycle. For the odd cycles the gap is a little earlier, 1332 – 1445 days after the start of the cycles with no extension. We analyze geomagnetic disturbances for Solar Cycles 17 – 24 using the Dst-index, the related Dxt- and Dcx-indices, and the Ap-index. In all of these time series there is a decline at the time, or somewhat after, the GG in the solar indices, and it is at its deepest between 1567 – 1725 days for the even cycles and between 1445 – 1567 days for the odd cycles. The averages of these indices for even cycles in the interval 1445 – 1725 are 46%, 46%, 18%, and 29% smaller compared to surrounding intervals of similar length for Dst, Dxt, Dcx, and Ap, respectively. For odd cycles the averages of the Dst- and Dxt-indices between 1322 – 1567 days are 31% and 12% smaller than the surrounding intervals, but not smaller for the Dcx-index and only 4% smaller for the Ap-index. The declines are significant at the 99% level for both even and odd cycles of the Dst-index and for the Dxt-, Dcx- and Ap-indices for even cycles. For odd cycles of the Dxt-index the significance is 95%, but the decline is insignificant for odd cycles of the Dcx- and Ap-indices.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Katerina G. Tsakiri ◽  
Antonios E. Marsellos ◽  
Igor G. Zurbenko
Keyword(s):  
New York ◽  
Time Series ◽  
Water Discharge ◽  
Winter Period ◽  
Statistical Methodology ◽  
Summer Period ◽  
Short Term ◽  
Efficient Prediction ◽  
Climatic Time Series

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

scholarly journals Track Irregularity Time Series Analysis and Trend Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wang Futian ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Measuring Point ◽  
Trend Forecasting ◽  
Changing Trend ◽  
Track Irregularity ◽  
Linear Differential

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

scholarly journals Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

2018 ◽  
Vol 7 (4.15) ◽  
pp. 25 ◽  
Author(s):  
Said Jadid Abdulkadir ◽  
Hitham Alhussian ◽  
Muhammad Nazmi ◽  
Asim A Elsheikh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.  

Paleocommunity information retrieval vs. shell accumulation mode in Paleozoic carbonates: examples from the Lebanon Limestone (Middle Ordovician), Tennessee, U.S.A.

1992 ◽  
Vol 6 ◽  
pp. 81-81
Author(s):  
David J. Davies ◽  
Molly F. Miller
Keyword(s):  
Information Retrieval ◽  
High Energy ◽  
Sediment Surface ◽  
Time Averaging ◽  
Short Term ◽  
Diagenetic Alteration ◽  
Shell Beds ◽  
Carbonate Shell ◽  
Shell Bed

Compared to their terrigenous counterparts, carbonate shell accumulations have until recently been relatively little studied to determine either descriptive or genetic classifications of shell bed types, the preservation potential of each type, or their relative ability to preserve community-level information. A partial classification of Paleozoic carbonate shell-rich soft sediment accumulations is proposed using sedimentation patterns in the Lebanon limestone of the Stones River Group. Paleoecological information preserved therein is then contrasted by shell bed type. The Lebanon represents typical Ordovician shallow to moderate subtidal carbonate shelf deposits in outcrops flanking the Nashville Dome and peritidal deposits in the Sequatchie Anticline of Eastern Tennessee; shell beds alternate with shell poor sediments (micrites, wackestones and diagenetically enhanced dolomites and clay-rich partings).None of the analyzed shell beds was strictly biological in origin; most are sedimentological although >10% are combined sedimentological/diagenetic. While the majority are single simple shell beds, >20% are amalgamated. All are thin (1 shell to 15 cm) stringers that pinch and swell showing poor lateral continuity (outcrop scale, tens to hundreds of meters) likely enhanced by burial dissolution. These shell beds differ greatly in fabric (packing/sorting), clast composition, taphonomic signature, and intensity of time averaging; thus community information retrieval is biased in predictable patterns. Virtually no shell beds show common shell dissolution or encrustation from long-term sediment surface exposure or hardground formation. Five major categories of accumulation are herein proposed using a DESCRIPTIVE, non-genetic terminology modified from previous works of DJD, as well as a Genetic interpretation for each. These are easily distinguished in the field and are also discriminated by Q-mode cluster analysis.Categories include, in decreasing frequency of occurrence: 1. SHELL GRAVELS; Storm/“event” beds: Sharp bases; poorly sorted coarse basal bioclasts and/or intraclasts, often with no preferred orientation; clasts fine upward to comminuted shell material and micrite. Horizontal platy brachiopods often cap the beds. High diversity and a wide range in shell alteration is represented, from whole unaltered brachiopods to minor abraded fragments, indicating extreme time averaging and poor resolution of short-term community dynamics. 2. COMMINUTED SHELLY LS; Current/ripple concentrations: Small tidal channel fill and discrete ripple trough accumulations are composed of cross-stratified bioclastic deposits with local concentrations of rip-ups. Beds are not graded; typically clasts are abraded, rounded and concordant with cross-beds. Intense time averaging and mixing of discrete communities is inferred due to continual reworking in these background deposits. 3. SHELL/CEMENT LS; Early cementation beds: Intense early diagenetic alteration is inferred due to red discoloration and rapid intergranular cementation; some beds show diagenetic micritic rinds. Beds may be brecciated and show deep burial stylolitization cutting bioclasts and cement. They may represent zones of preferred early cementation rather than a change in shell accumulation rate. Many shells from some beds show little postmortem alteration; these units may preserve much of the original community structure. 4. DENSE SHELL PAVEMENTS; Subtidal surficial pavements: Single layers of shells, commonly concave down, overlie mudstones/wackestones with no basal erosion. No obrution deposits were noted. Bioclasts are typically disarticulated and reoriented, but are not substantially abraded, broken, or dissolved. Diversity is low. Only minor temporal and lateral community mixing with small environmental fluctuation is indicated. 5. VERTICALLY IMBRICATE SHELLY LS; High energy beach zones: Platy whole and major fragments of brachiopods are deposited in low diversity, high angle imbricate beds. Less postmortem reworking and time averaging is evident compared to types 1 and 2.Thus, the most common (physically reworked) shell bed types show the most intense loss of short-term paleocommunity information. There are surprisingly few insitu community pavements or obligate long-term accumulations. This pattern differs from some described Ordovician carbonates, which may contain common community beds or hardgrounds/hiatal accumulations. This implies a relatively low rate of net sediment accumulation on a shallow, periodically wave swept shelf, and no major flooding surfaces or other indications of significant sea level change. Delineation of the sequence stratigraphic position of these carbonates is enhanced from this type of integrated community/biostratinomic analysis.

Time Series Forecasting in Retail Sales Using LSTM and Prophet

2021 ◽  
pp. 241-262
Author(s):  
Clony Junior ◽  
Pedro Gusmão ◽  
José Moreira ◽  
Ana Maria M. Tome
Keyword(s):  
Time Series ◽  
Data Science ◽  
Short Term Memory ◽  
Special Kind ◽  
Time Series Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Use Of Time

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

Long-Term Area Statistics for Maximum Crest Height Under a Fixed Platform Deck

2018 ◽  
Author(s):  
Øistein Hagen ◽  
Jørn Birknes-Berg ◽  
Ida Håøy Grue ◽  
Gunnar Lian ◽  
Kjersti Bruserud ◽  
...  
Keyword(s):  
Time Series ◽  
North Sea ◽  
Extreme Values ◽  
Second Order ◽  
Irregular Waves ◽  
Scatter Diagram ◽  
Short Term ◽  
Wave Impact ◽  
Crest Height

As offshore reservoirs are depleted, the seabed may subside. Furthermore, the extreme crests estimates are now commonly higher than obtained previously due to improved understanding of statistics of non-linear irregular waves. Consequently, bottom fixed installations which have previously had sufficient clearance between the deck and the sea surface may be in a situation where wave impact with the deck must be considered at relevant probability levels. In the present paper, we investigate the long-term area statistics for maximum crest height under a fixed platform deck for 2nd order short crested and long crested sea based on numerical simulations as a function of platform deck dimension for jackets. The results are for one location in the northern North Sea, but some key results are also reported and verified for a more benign southern North Sea location. Time domain simulations for long crested and short crested waves over a spatial domain with dimension of a platform deck are performed, and relevant statistics for airgap assessment determined. Second order waves are simulated for the different cells in the (Hs, Tp) scatter diagram for Torsethaugen two-peak wave spectrum for long-crested and short-crested sea. A total of 1000 3-hour sea states are generated per cell, and time series generated for 160 spatial points under a platform deck. Short-term and long-term statistics are established for the maximum crest height as function of platform dimension; inline and transverse to the wave direction, and over the area. Results are given for the linear sea and for the second order time series. The annual q-probability estimates for the maximum crest height over area as a function of platform dimension is determined for a location at the Norwegian Continental Shelf by weighting the short-term statistics for the individual cells in the scatter diagram with the long-term probability of occurrence of the sea state. To reduce the number of numerical second order simulations, the effect of excluding cells that have a negligible effect on the long term extreme crest estimate is discussed. The percentiles in the distribution of maximum crest (over area) in design sea states that corresponds to the extreme values obtained from the long-term analysis are determined for long crested and short crested sea. The increase in the extreme crest over an area compared to the point in space estimate is estimated for both linear and second order surface elevation.

scholarly journals Novel FTLRNN with Gamma Memory for Short-Term and Long-Term Predictions of Chaotic Time Series

2009 ◽  
Vol 2009 ◽  
pp. 1-21
Author(s):  
Sanjay L. Badjate ◽  
Sanjay V. Dudul
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mean Squared Error ◽  
Chaotic Time Series ◽  
Nonstationary Time Series ◽  
Medium Term ◽  
Short Term ◽  
Squared Error ◽  
Wide Range

Multistep ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in the recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep chaotic time series prediction. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time-lagged recurrent neural network (FTLRNN) model with gamma memory is developed for different prediction horizons. It is observed that this predictor performs remarkably well for short-term predictions as well as medium-term predictions. For coupled partial differential equations generated chaotic time series such as Mackey Glass and Duffing, FTLRNN-based predictor performs consistently well for different depths of predictions ranging from short term to long term, with only slight deterioration after k is increased beyond 50. For real-world highly complex and nonstationary time series like Sunspots and Laser, though the proposed predictor does perform reasonably for short term and medium-term predictions, its prediction ability drops for long term ahead prediction. However, still this is the best possible prediction results considering the facts that these are nonstationary time series. As a matter of fact, no other NN configuration can match the performance of FTLRNN model. The authors experimented the performance of this FTLRNN model on predicting the dynamic behavior of typical Chaotic Mackey-Glass time series, Duffing time series, and two real-time chaotic time series such as monthly sunspots and laser. Static multi layer perceptron (MLP) model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE), and Correlation Coefficient (r). The standard back-propagation algorithm with momentum term has been used for both the models.

Sign in / Sign up

Export Citation Format

Share Document