scholarly journals How to correctly apply Gaussian statistics in a non-stationary climate?

2021 ◽  
Author(s):  
Reinhold Steinacker
Keyword(s):  
Time Series ◽  
Actual Data ◽  
Temperature Record ◽  
Positive Trend ◽  
Positive Temperature ◽  
Climatic Trend ◽  
Filter Methods ◽  
Periodic Time Series ◽  
Past Data

AbstractTime series with a significant trend, as is now being the case for the temperature in the course of climate change, need a careful approach for statistical evaluations. Climatological means and moments are usually taken from past data which means that the statistics does not fit to actual data anymore. Therefore, we need to determine the long-term trend before comparing actual data with the actual climate. This is not an easy task, because the determination of the signal—a climatic trend—is influenced by the random scatter of observed data. Different filter methods are tested upon their quality to obtain realistic smoothed trends of observed time series. A new method is proposed, which is based on a variational principle. It outperforms other conventional methods of smoothing, especially if periodic time series are processed. This new methodology is used to test, how extreme the temperature of 2018 in Vienna actually was. It is shown that the new annual temperature record of 2018 is not too extreme, if we consider the positive trend of the last decades. Also, the daily mean temperatures of 2018 are not found to be really extreme according to the present climate. The real extreme of the temperature record of Vienna—and many other places around the world—is the strongly increased positive temperature trend over the last years.

scholarly journals Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous

2021 ◽  
Vol 4 (1) ◽  
pp. 57
Author(s):  
Tito Tatag Prakoso ◽  
Etik Zukhronah ◽  
Hasih Pratiwi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Series Data ◽  
Gregorian Calendar ◽  
Time Series Regression ◽  
Autoregressive Integrated Moving Average ◽  
Past Data

<p>Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous  (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.</p><strong>Keywords: </strong>time series regression, seasonal, calendar variations, SARIMAX, forecasting

scholarly journals Model Heuristic Time Invariant Fuzzy Time Series dan Regresi Untuk Prediksi Laba dan Analisis Variabel yang Mempengaruhi

2016 ◽  
Vol 6 (2) ◽  
pp. 144
Author(s):  
Ica Admirani ◽  
Rachmat Gernowo ◽  
Suryono Suryono
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Fuzzy Time Series ◽  
Actual Data ◽  
Percentage Error ◽  
Time Series Method ◽  
Past Data ◽  
Administrative Expenses ◽  
Time Invariant ◽  
Fuzzy Logical

Model of prediction with fuzzy time series method has ability to capture the pattern of past data to predict the fu ture of data does not need a complicated system, making it easier to use. The research aims to built prediction system using model of  heuristic time invariant fuzzy time series and multiple linear regression to predict profit and analysis of variables that affect profit. Profit forecasting aims to determine the company's prospects in the future in order to remain exist in doing its business. The variables that use in the modelling are profit as the dependent variable, and sales, cost of goods sold, general and administrative expenses, selling and marketing expenses and interest income as the indepent variables. Profit forecasting modelling begins by defining universe of discourse and interval actual data of profit, then determine fuzzy set and actual data fuzzified. Furthermore, fuzzy logical relationship and fuzzy logical relationships group to fuzzified data. The prediction process consist of two prediction phase there are training phase aimed to determine trend predictor and testing phase to determine prediction results. By using 24 profit data samples resulted prediction error by using Mean Absolute Percentage Error is 11,64% and added 13 data for testing obtained prediction error is 22,27%.  In analysis of variables that affect profit is known that sales variable most effect on profit than other variables with a regression coefficient 0.976.

scholarly journals Time Series Modelling of Monthly Temperature and Reference Evapotranspiration for Navsari (Gujarat), India

2019 ◽  
pp. 1-13
Author(s):  
D. K. Dwivedi ◽  
P. K. Shrivastava
Keyword(s):  
Time Series ◽  
Reference Evapotranspiration ◽  
Kendall Test ◽  
Trend Detection ◽  
Positive Trend ◽  
Monthly Temperature ◽  
Mean Temperature ◽  
Time Series Modelling ◽  
Mann Kendall Test ◽  
Past Data

Time series modelling has been proved its usefulness in various fields including meteorology, hydrology and agriculture. It utilizes past data and extracts useful information from them to build up a model which could simulate various processes. The prior knowledge of evapotranspiration could help in estimating the amount of water required by the crops that is useful for optimizing design of irrigation systems. In this study, the time series modelling of monthly temperature and reference evapotranspiration has been carried out utilizing past data of 35 years (1983-2017) to assist decision makers related to agriculture and meteorology. 30 years (1983-2012) of temperature and evapotranspiration data were used for training and remaining 5 years of data (2013-2017) were used for validation. The monthly evapotranspiration was estimated using Penman-Monteith FAO-56 method. Mann-Kendall test was used at 5% significant level for identifying trend component in mean temperature. The time series of temperature and evapotranspiration was made stationary for modelling the stochastic components using ARIMA (Autoregressive Integrated Moving Average) model. In order to check the normality of residuals, the Portmantaeu test was applied. The time series models for temperature and evapotranspiration which were validated for 5 years (2013-2017) and further deployed for forecasting of 5 years (2018-2022). It was found that for modelling temperature and reference evapotranspiration for Navsari, seasonal ARIMA (1,0,0)(0,1,1)12 and seasonal ARIMA (1,0,1)(1,1,2)12 were found to be appropriate models respectively. Mann Kendall test used for trend detection in monthly mean temperature revealed that October and November months had significant positive trend. Negative trend was observed only in the month of June.

New determinations of tides on the north-western Ross Ice Shelf

2020 ◽  
pp. 1-14
Author(s):  
Richard D. Ray ◽  
Kristine M. Larson ◽  
Bruce J. Haines
Keyword(s):  
Time Series ◽  
Tide Gauge ◽  
High Rate ◽  
Ice Shelf ◽  
Ross Ice Shelf ◽  
The North ◽  
Global Positioning ◽  
North Western

Abstract New determinations of ocean tides are extracted from high-rate Global Positioning System (GPS) solutions at nine stations sitting on the Ross Ice Shelf. Five are multi-year time series. Three older time series are only 2–3 weeks long. These are not ideal, but they are still useful because they provide the only in situ tide observations in that sector of the ice shelf. The long tide-gauge observations from Scott Base and Cape Roberts are also reanalysed. They allow determination of some previously neglected tidal phenomena in this region, such as third-degree tides, and they provide context for analysis of the shorter datasets. The semidiurnal tides are small at all sites, yet M2 undergoes a clear seasonal cycle, which was first noted by Sir George Darwin while studying measurements from the Discovery expedition. Darwin saw a much larger modulation than we observe, and we consider possible explanations - instrumental or climatic - for this difference.

scholarly journals Landslide geometry and activity in Villa de la Independencia (Bolivia) revealed by InSAR and seismic noise measurements

Landslides ◽  
2021 ◽  
Author(s):  
Chuang Song ◽  
Chen Yu ◽  
Zhenhong Li ◽  
Veronica Pazzi ◽  
Matteo Del Soldato ◽  
...  
Keyword(s):  
Time Series ◽  
Seismic Noise ◽  
Slip Surface ◽  
Inversion Method ◽  
Subsurface Structures ◽  
Slip Surfaces ◽  
Landslide Volume ◽  
Noise Measurements ◽  
Landslide Body

AbstractInterferometric Synthetic Aperture Radar (InSAR) enables detailed investigation of surface landslide movements, but it cannot provide information about subsurface structures. In this work, InSAR measurements were integrated with seismic noise in situ measurements to analyse both the surface and subsurface characteristics of a complex slow-moving landslide exhibiting multiple failure surfaces. The landslide body involves a town of around 6000 inhabitants, Villa de la Independencia (Bolivia), where extensive damages to buildings have been observed. To investigate the spatial-temporal characteristics of the landslide motion, Sentinel-1 displacement time series from October 2014 to December 2019 were produced. A new geometric inversion method is proposed to determine the best-fit sliding direction and inclination of the landslide. Our results indicate that the landslide is featured by a compound movement where three different blocks slide. This is further evidenced by seismic noise measurements which identified that the different dynamic characteristics of the three sub-blocks were possibly due to the different properties of shallow and deep slip surfaces. Determination of the slip surface depths allows for estimating the overall landslide volume (9.18 · 107 m3). Furthermore, Sentinel-1 time series show that the landslide movements manifest substantial accelerations in early 2018 and 2019, coinciding with increased precipitations in the late rainy season which are identified as the most likely triggers of the observed accelerations. This study showcases  the potential of integrating InSAR and seismic noise techniques to understand the landslide mechanism from ground to subsurface.

scholarly journals Linguistic summaries of locally periodic time series

2016 ◽  
Vol 285 ◽  
pp. 94-117 ◽  
Author(s):  
Gilles Moyse ◽  
Marie-Jeanne Lesot
Keyword(s):  
Time Series ◽  
Periodic Time Series

A comparative study of methods to reconstruct a periodic time series from an environmental proxy record

2009 ◽  
Vol 95 (3-4) ◽  
pp. 97-118 ◽  
Author(s):  
Anouk de Brauwere ◽  
Fjo De Ridder ◽  
Rik Pintelon ◽  
Johan Schoukens ◽  
Frank Dehairs
Keyword(s):  
Time Series ◽  
Comparative Study ◽  
Proxy Record ◽  
Periodic Time Series ◽  
Environmental Proxy

Economic Analysis of Production of Maize in Meghalaya and Constraints Associated with it

2021 ◽  
Vol 66 (3) ◽  
Author(s):  
Dipriya R. Lyngkhoi
Keyword(s):  
Plant Protection ◽  
Weather Conditions ◽  
Cost Ratio ◽  
Positive Trend ◽  
Maize Production ◽  
Net Return ◽  
Pests And Diseases ◽  
Average Return ◽  
Maize Cultivation

The present study was undertaken to estimate the costs and returns structure of maize cultivation and identifying the prominent production constraints in West Khasi Hills district of Meghalaya. A sample of 60 farmers was randomly drawn from six villages of Mawthadraishan and Nongstoin block of the selected district. The costs and returns per hectare were calculated on the basis of cost concepts and Garett ranking method was used for employed for determination of constraints in maize production. The overall cost of cultivation was found to be ` 37185.22 per ha and the major cost components were manures (48.25%) and human labour (34.73%). The overall net return was evaluated at ` 19038.20 with small, medium and large farmers having similar returns with the exception of marginal farmers gaining a net return of only ` 13889.83 which was 27.04 per cent lower than the average return among the sample farmers. It may be attributed to their heavy dependence on labour and lack of investment on irrigation, plant protection and better-quality seeds. The realised average yield was found to be 23.65 q/ha which was abysmally low compared to other maize producing states of India. A positive trend between the return over cost ratio and the operational holding was observed with an average of 1.51. The prominent constraints as perceived by the farmers were unfavorable weather conditions, the incidence of pests and diseases and costly fertilizers and manures with the Garrett’s score of 64.70, 62.75 and 54.40 respectively

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