scholarly journals An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations

2021 ◽  
Vol 14 (9) ◽  
pp. 6119-6135
Author(s):  
Alex Resovsky ◽  
Michel Ramonet ◽  
Leonard Rivier ◽  
Jerome Tarniewicz ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Time Series ◽  
Atmospheric Transport ◽  
Anomalous Behavior ◽  
Synoptic Scale ◽  
Short Term ◽  
Noise Component ◽  
Statistical Framework ◽  
Polynomial Fit ◽  
Daily Data ◽  
Tall Tower

Abstract. We present a statistical framework to identify regional signals in station-based CO2 time series with minimal local influence. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO2 cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally adjusted noise component, equal to 2 standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which surpass this ±2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale atmospheric transport events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts.

scholarly journals An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations

2021 ◽  
Author(s):  
Alex Resovsky ◽  
Michel Ramonet ◽  
Leonard Rivier ◽  
Jerome Tarniewicz ◽  
Philippe Ciais ◽  
...  
Keyword(s):  
Time Series ◽  
Anomalous Behavior ◽  
Time Signal ◽  
Synoptic Scale ◽  
Short Term ◽  
Noise Component ◽  
Statistical Framework ◽  
Polynomial Fit ◽  
Daily Data ◽  
Weather Events

Abstract. We present a statistical framework for near real-time signal processing to identify regional signals in CO2 time series recorded at stations which are normally uninfluenced by local processes. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO2 cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally-adjusted noise component, equal to two standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which rise above this 2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale weather events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts.

Influence of Currents and Atmospheric Circulation on the Efficiency of European Anchovy Fishing during its Wintering off the Black Sea Coast of the Krasnodar Territory

2021 ◽  
Author(s):  
B. N. Panov ◽  
E. O. Spiridonova ◽  
◽  
Keyword(s):  
Black Sea ◽  
Atmospheric Transport ◽  
Mesoscale Eddy ◽  
The Black Sea ◽  
Short Term ◽  
European Anchovy ◽  
The North ◽  
Daily Data ◽  
Black Sea Coast ◽  
Sea Coast

Russian fishermen harvest European anchovy primarily off the Black Sea coast of the Krasnodar Territory during its wintering and wintering migrations. At wintering grounds, temperature conditions become a secondary factor in determining the behaviour of commercial concentration of European anchovy, with wind and currents being the primary factors. Therefore, the aim of this work is to determine the potential use of daily data on water circulation and local atmospheric transport in short-term (1–7 days) forecasting of European anchovy fishing in the Black Sea. The research used the European anchovy fishery monitoring materials for January – March 2019, as well as daily maps of the Black and Azov Seas level anomalies (from satellite altimetry data) and surface atmospheric pressure and temperature in Europe (analysis) for the mentioned period. The dynamics of the catch rate and its relation to altimetry and atmospheric transport indicators in the north-eastern part of the Black Sea were investigated using graphical and correlation methods. This analysis showed that the main factor contributing to increased catches is intensification of northwest currents in the coastal 60-km zone. The effect of atmospheric transport on fishing efficiency depends on the mesoscale eddy structure of the nearshore current field. In the presence of an intense northwest current in the fishing area, southwest atmospheric transports have a positive effect on fishing, while in the presence of an anticyclonic meander of currents, northeast atmospheric transports become effective. The presence of maximum significant relationships when the determinants of fishing performance are shifted by 1–7 days allows making short-term predictions of fishing efficiency.

scholarly journals Bitcoin Analysis and Forecasting through Fuzzy Transform

Axioms ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 139
Author(s):  
Maria Letizia Guerra ◽  
Laerte Sorini ◽  
Luciano Stefanini
Keyword(s):  
Time Series ◽  
Sentiment Analysis ◽  
Test Statistics ◽  
Local Smoothing ◽  
Fuzzy Transform ◽  
Short Term ◽  
Stylized Facts ◽  
Lp Norm ◽  
Daily Data ◽  
Short Term Forecasting

Sentiment analysis to characterize the properties of Bitcoin prices and their forecasting is here developed thanks to the capability of the Fuzzy Transform (F-transform for short) to capture stylized facts and mutual connections between time series with different natures. The recently proposed Lp-norm F-transform is a powerful and flexible methodology for data analysis, non-parametric smoothing and for fitting and forecasting. Its capabilities are illustrated by empirical analyses concerning Bitcoin prices and Google Trend scores (six years of daily data): we apply the (inverse) F-transform to both time series and, using clustering techniques, we identify stylized facts for Bitcoin prices, based on (local) smoothing and fitting F-transform, and we study their time evolution in terms of a transition matrix. Finally, we examine the dependence of Bitcoin prices on Google Trend scores and we estimate short-term forecasting models; the Diebold–Mariano (DM) test statistics, applied for their significance, shows that sentiment analysis is useful in short-term forecasting of Bitcoin cryptocurrency.

Using robust baseline extraction to examine synoptic-scale variability in European CO2

2020 ◽  
Author(s):  
Alex Resovsky ◽  
Michel Ramonet ◽  
Leonard Rivier ◽  
Sebastien Conil ◽  
Gerard Spain
Keyword(s):  
Time Series ◽  
Atmospheric Transport ◽  
Transport Processes ◽  
Surface Fluxes ◽  
Data Availability ◽  
Observation System ◽  
Back Trajectory ◽  
Synoptic Scale ◽  
Source Sink ◽  
Baseline Estimation

<p>Continuous measurements of long-lived greenhouse gases at ground-based monitoring stations are frequently influenced by regional surface fluxes and atmospheric transport processes, which induce variability at a range of timescales.  Dissecting this variability is critical to identifying long-term trends and understanding regional source-sink patterns, but it requires a robust characterization of the underlying signal comprising the background air composition at a given site.  Methods of background signal extraction that make use of chemical markers or meteorological filters yield reliable estimates, but often must be adapted for site-specific measurement conditions and data availability.  Statistical baseline extraction tools provide a more generally transferable alternative to such methods.  Here, we apply one such technique (REBS) to a continuous time series of atmospheric CO<sub>2</sub> readings at Mace Head, Ireland and compare the results to a modeled baseline signal obtained from local wind observations. We then assess REBS’ performance at two continental sites within the Integrated Carbon Observation System (ICOS) network at which baseline signals are derived using back-trajectory analyses.  Overall, we find that REBS effectively reduces the bias in wintertime baseline estimation relative to other statistical techniques, and thus represents a computationally inexpensive and transferable approach to baseline extraction in atmospheric time series. To investigate one potential application of such an approach, we examine wintertime synoptic-scale CO<sub>2</sub> excursions from the REBS baseline during the period 2015-2019.  Our goal is to identify relationships between the timing and strength of such events and to better understand sub-seasonal variability in CO<sub>2</sub> transport over Europe.</p>

scholarly journals ARIMA Model in Predicting Banking Stock Market Data

2018 ◽  
Vol 12 (11) ◽  
pp. 309 ◽  
Author(s):  
Mohammad Almasarweh ◽  
S. AL Wadi
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
Short Term Prediction

Banking time series forecasting gains a main rule in finance and economics which has encouraged the researchers to introduce a fit models in forecasting accuracy. In this paper, the researchers present the advantages of the autoregressive integrated moving average (ARIMA) model forecasting accuracy. Banking data from Amman stock market (ASE) in Jordan was selected as a tool to show the ability of ARIMA in forecasting banking data. Therefore, Daily data from 1993 until 2017 is used for this study. As a result this article shows that the ARIMA model has significant results for short-term prediction. Therefore, these results will be helpful for the investments.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals Short-term household load forecasting based on Long- and Short-term Time-series network

2021 ◽  
Vol 7 ◽  
pp. 58-64
Author(s):  
Xifeng Guo ◽  
Ye Gao ◽  
Yupeng Li ◽  
Di Zheng ◽  
Dan Shan
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Short Term

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.

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