scholarly journals Time series forecasting: Obtaining long term trends with self-organizing maps

2005 ◽  
Vol 26 (12) ◽  
pp. 1795-1808 ◽  
Author(s):  
G. Simon ◽  
A. Lendasse ◽  
M. Cottrell ◽  
J.-C. Fort ◽  
M. Verleysen
Keyword(s):  
Time Series ◽  
Time Series Forecasting ◽  
Self Organizing Maps ◽  
Long Term Trends ◽  
Self Organizing

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

scholarly journals Spatial and Temporal Spread of the COVID-19 Pandemic Using Self Organizing Neural Networks and a Fuzzy Fractal Approach

2021 ◽  
Vol 13 (15) ◽  
pp. 8295
Author(s):  
Patricia Melin ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Temporal Trends ◽  
Main Idea ◽  
Temporal Analysis ◽  
Virus Propagation ◽  
Self Organizing Maps ◽  
Fractal Approach ◽  
The Times ◽  
Self Organizing

In this article, the evolution in both space and time of the COVID-19 pandemic is studied by utilizing a neural network with a self-organizing nature for the spatial analysis of data, and a fuzzy fractal method for capturing the temporal trends of the time series of the countries considered in this study. Self-organizing neural networks possess the capability to cluster countries in the space domain based on their similar characteristics, with respect to their COVID-19 cases. This form enables the finding of countries that have a similar behavior, and thus can benefit from utilizing the same methods in fighting the virus propagation. In order to validate the approach, publicly available datasets of COVID-19 cases worldwide have been used. In addition, a fuzzy fractal approach is utilized for the temporal analysis of the time series of the countries considered in this study. Then, a hybrid combination, using fuzzy rules, of both the self-organizing maps and the fuzzy fractal approach is proposed for efficient coronavirus disease 2019 (COVID-19) forecasting of the countries. Relevant conclusions have emerged from this study that may be of great help in putting forward the best possible strategies in fighting the virus pandemic. Many of the existing works concerned with COVID-19 look at the problem mostly from a temporal viewpoint, which is of course relevant, but we strongly believe that the combination of both aspects of the problem is relevant for improving the forecasting ability. The main idea of this article is combining neural networks with a self-organizing nature for clustering countries with a high similarity and the fuzzy fractal approach for being able to forecast the times series. Simulation results of COVID-19 data from countries around the world show the ability of the proposed approach to first spatially cluster the countries and then to accurately predict in time the COVID-19 data for different countries with a fuzzy fractal approach.

Mercury trends and cycling in northern Wisconsin related to atmospheric and hydrologic processes

2019 ◽  
Vol 76 (5) ◽  
pp. 831-846 ◽  
Author(s):  
C.J. Watras ◽  
D. Grande ◽  
A.W. Latzka ◽  
L.S. Tate
Keyword(s):  
Time Series ◽  
Water Cycle ◽  
Water Levels ◽  
Hydrologic Processes ◽  
Littoral Sediments ◽  
Long Term Trends ◽  
Acid Base Status ◽  
Explanatory Mechanism ◽  
Regional Water

Atmospheric deposition is the principal source of mercury (Hg) to remote northern landscapes, but its fate depends on multiple factors and internal feedbacks. Here we document long-term trends and cycles of Hg in the air, precipitation, surface water, and fish of northern Wisconsin that span the past three decades, and we investigate relationships to atmospheric processes and other variables, especially the regional water cycle. Consistent with declining emission inventories, there was evidence of declining trends in these time series, but the time series for Hg in some lakes and most fish were dominated by a near-decadal oscillation that tracked the regional oscillation of water levels. Concentrations of important solutes (SO4, dissolved organic carbon) and the acid–base status of lake water also tracked water levels in ways that cannot be attributed to simple dilution or concentration. The explanatory mechanism is analogous to the “reservoir effect” wherein littoral sediments are periodically exposed and reflooded, altering the internal cycles of sulfur, carbon, and mercury. These climatically driven, near-decadal oscillations confound short or sparse time series and complicate relationships among Hg emissions, deposition, and bioaccumulation.

Warming is Driving Decreases in Snow Fractions While Runoff Efficiency Remains Mostly Unchanged in Snow-Covered Areas of the Western United States

2018 ◽  
Vol 19 (5) ◽  
pp. 803-814 ◽  
Author(s):  
Gregory J. McCabe ◽  
David M. Wolock ◽  
Melissa Valentin
Keyword(s):  
United States ◽  
Time Series ◽  
Winter Precipitation ◽  
Western United States ◽  
Balance Model ◽  
Winter Temperatures ◽  
Long Term Trends ◽  
Monthly Water Balance ◽  
Surface Water Supply

Abstract Winter snowfall and accumulation is an important component of the surface water supply in the western United States. In these areas, increasing winter temperatures T associated with global warming can influence the amount of winter precipitation P that falls as snow S. In this study we examine long-term trends in the fraction of winter P that falls as S (Sfrac) for 175 hydrologic units (HUs) in snow-covered areas of the western United States for the period 1951–2014. Because S is a substantial contributor to runoff R across most of the western United States, we also examine long-term trends in water-year runoff efficiency [computed as water-year R/water-year P (Reff)] for the same 175 HUs. In that most S records are short in length, we use model-simulated S and R from a monthly water balance model. Results for Sfrac indicate long-term negative trends for most of the 175 HUs, with negative trends for 139 (~79%) of the HUs being statistically significant at a 95% confidence level (p = 0.05). Additionally, results indicate that the long-term negative trends in Sfrac have been largely driven by increases in T. In contrast, time series of Reff for the 175 HUs indicate a mix of positive and negative long-term trends, with few trends being statistically significant (at p = 0.05). Although there has been a notable shift in the timing of R to earlier in the year for most HUs, there have not been substantial decreases in water-year R for the 175 HUs.

Availability and Sales of Alcohol in Four Canadian Provinces: A Time-Series Analysis

2005 ◽  
Vol 32 (3) ◽  
pp. 343-372 ◽  
Author(s):  
Björn Trolldal
Keyword(s):  
Time Series ◽  
Research Question ◽  
Pure Alcohol ◽  
Dependent Variables ◽  
Long Term Trends ◽  
Time Series Analyses ◽  
Canadian Provinces ◽  
Physical Availability ◽  
Total Sales

The research question addressed in the present study, with ARIMA time-series analyses, was the extent to which changes in economic and physical availability had an effect on sales of alcohol in four Canadian provinces during the second half of the 20th century. The annual sales, by type of beverage (spirits, wine and beer) as well as total sales, measured in pure alcohol per inhabitant age 15 and above in each province, were used as dependent variables in the analyses. The inhabitants' real disposable income, the real price of alcohol, and the number of on- and off-premise outlets per 100,000 inhabitants were used as independent variables. All the time-series were differenced to remove long-term trends. The main study period was 1951–2000. In some of the analyses the study periods were shorter, primarily due to lack of data. Changes in economic availability in general, and in price in particular, had larger effects on sales than physical availability. Among the beverages analyzed in the study, the demand for spirits was most sensitive to changes in availability. Economic availability had a greater effect on sales than the number of outlets. However, one might question to what extent the number of outlets really is a feasible measure of transaction costs associated with purchases of alcohol.

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