scholarly journals Modelling long memory in maximum and minimum temperature series in India

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 317-326
Author(s):  
RANJIT KUMAR PAUL
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Minimum Temperature ◽  
Fractional Integration ◽  
Predictive Ability ◽  
Temperature Series ◽  
Weather Data ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Temperature Pattern

Time series analysis of weather data can be a very valuable tool to investigate its variability pattern and, maybe, even to predict short- and long-term changes in the time series. In this study, the long memory behaviour of monthly minimum and maximum temperature of India for the period 1901 to 2007 by means of fractional integration techniques has been investigated. The results show that the time series can be specified in terms of autoregressive fractionally integrated moving average (ARFIMA) process. Both the series were found to be integrated with orders of integration smaller than 0.5 ensuring the long memory stationarity. Wavelet methodology in frequency domain with Haar wavelet filter was applied in order to see the oscillation at different scale and at different time epochs of the series. Multiresolution analysis (MRA) was carried out to explore the local as well as global variations in both the temperature series over the years. The variability in minimum temperature is found to be more than maximum temperature. Though there is no clear significance trend in the temperature series in the long run, but there are pockets of change in the temperature pattern. The predictive ability of ARFIMA model was investigated in terms of relative mean absolute percentage error.

Local Whittle estimation of the long-memory parameter

2020 ◽  
Vol 20 (3) ◽  
pp. 565-583
Author(s):  
Christopher F. Baum ◽  
Stan Hurn ◽  
Kenneth Lindsay
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Fractional Integration ◽  
Whittle Estimation ◽  
Memory Parameter

In this article, we describe and implement the local Whittle and exact local Whittle estimators of the order of fractional integration of a time series.

scholarly journals TESTING THE ORDER OF FRACTIONAL INTEGRATION OF A TIME SERIES IN THE POSSIBLE PRESENCE OF A TREND BREAK AT AN UNKNOWN POINT

2018 ◽  
Vol 35 (6) ◽  
pp. 1201-1233 ◽  
Author(s):  
Fabrizio Iacone ◽  
Stephen J. Leybourne ◽  
A.M. Robert Taylor
Keyword(s):  
Time Series ◽  
Power Function ◽  
Long Memory ◽  
Fractional Integration ◽  
Monte Carlo Study ◽  
Local Power ◽  
Finite Sample ◽  
Lm Test ◽  
Unknown Point ◽  
Memory Parameter

We develop a test, based on the Lagrange multiplier [LM] testing principle, for the value of the long memory parameter of a univariate time series that is composed of a fractionally integrated shock around a potentially broken deterministic trend. Our proposed test is constructed from data which are de-trended allowing for a trend break whose (unknown) location is estimated by a standard residual sum of squares estimator applied either to the levels or first differences of the data, depending on the value specified for the long memory parameter under the null hypothesis. We demonstrate that the resulting LM-type statistic has a standard limiting null chi-squared distribution with one degree of freedom, and attains the same asymptotic local power function as an infeasible LM test based on the true shocks. Our proposed test therefore attains the same asymptotic local optimality properties as an oracle LM test in both the trend break and no trend break environments. Moreover, this asymptotic local power function does not alter between the break and no break cases and so there is no loss in asymptotic local power from allowing for a trend break at an unknown point in the sample, even in the case where no break is present. We also report the results from a Monte Carlo study into the finite-sample behaviour of our proposed test.

Relationship of weather and plant factors to alfalfa bloat in autumn

1991 ◽  
Vol 71 (3) ◽  
pp. 861-866 ◽  
Author(s):  
J. W. Hall ◽  
W. Majak
Keyword(s):  
Key Words ◽  
Total Nitrogen ◽  
Minimum Temperature ◽  
Dry Matter ◽  
Extreme Case ◽  
Weather Data ◽  
Maximum Temperature ◽  
Soluble Nitrogen ◽  
Acid Detergent Fiber ◽  
Relationship Of

The bloat status of cattle was recorded in the autumns of 6 yr when bloat occurred during the decade 1979–1988. Weather data were available for all 6 yr, plant dry matter, acid detergent fiber and plant chlorophyll for 3 yr and plant total nitrogen and soluble nitrogen for 4 yr. The percentages of dry matter and acid detergent fiber were lower and the concentrations of chlorophyll, total nitrogen and soluble nitrogen were higher on days when bloat occurred than when it did not. There was no difference in minimum temperature classified by bloat status on the same day, or in maximum temperature, hours of sunshine or precipitation classified by bloat status on the next day. Hours of sunshine and the temperature range were greater on days when bloat occurred. Bloat was observed after "killing frosts" of −2.2 °C in all years and in an extreme case after a daily minimum of −9.6 °C. Key words: Legume, bloat, alfalfa, cattle, climate

scholarly journals Modelling International Monthly Tourist in Spain*

2020 ◽  
Vol 29 (3) ◽  
pp. 723-736
Author(s):  
Juncal Cuñado ◽  
Luis Alberiko Gil-Alana ◽  
Fernando Perez De Gracia
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Fractional Integration ◽  
Structural Break ◽  
A Value ◽  
Integration Techniques ◽  
The Mean

This article investigates the degree of persistence in the international monthly tourist time series in Spain using long memory (fractional integration) techniques. Our findings can be summarized as follows. The two standard hypotheses of integer degrees of differentiation, i.e., the I(0) and the I(1) behaviour, are clearly rejected. The series is found to be I(d) with a value of d in the interval (0.421, 0.780) thus implying long memory behaviour and mean reverting behaviour. However, if a structural break is considered, it takes place at May 2007, and then, the two subsamples present orders of integration which are above 1 and thus rejecting the mean reverting hypothesis.

scholarly journals A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

2020 ◽  
Vol 8 (7) ◽  
pp. 09-17
Author(s):  
Varsha M., Dr. Poornima B.
Keyword(s):  
Prediction Model ◽  
Minimum Temperature ◽  
Blast Disease ◽  
Weather Data ◽  
Maximum Temperature ◽  
Research Station ◽  
Disease Prediction ◽  
Features Selection ◽  
Ensemble Classifiers ◽  
Epidemic Disease

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather  data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifier based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers  4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved  accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease.

scholarly journals Assessing the Spatial Distribution of Biodiversity in a Changing Temperature Pattern: The Case of Catalonia, Spain

2019 ◽  
Vol 16 (20) ◽  
pp. 4026 ◽  
Author(s):  
Diego Varga ◽  
Mariona Roigé ◽  
Josep Pintó ◽  
Marc Saez
Keyword(s):  
Land Use ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Temperature Pattern ◽  
Explanatory Variables ◽  
Number Of Species ◽  
Protected Natural Areas ◽  
Planning Processes

The impacts that climate change and land-use dynamics have on biodiversity are already visible in the distribution and behaviour of a large number of species. By using a Bayesian framework, including land-use, meteorological, topography and other variables as explanatory variables, such as distance to roads and urban centres, we modeled a number of species within each cell of a regular lattice for Catalonia, Spain, in the period of 2004 to 2010. We estimated a slight increase in daily maximum temperature and a more significant increase in minimum temperature (a 5-year increase of 0.159 °C in maximum temperature, and an increase of 0.332 °C in minimum temperature). The estimation shows that the total number of species was greater than expected in the cells where land use was not urban—38.4%, in forests and 55.2% in mixed forests. Finally, we observed that most invasive species are found in areas where the minimum temperature is expected to increase. Our study can help with making important recommendations as to where, when and how future threats could affect specie distribution and the kind of planning processes needed for when protected natural areas will be unable to continue to support all the species they were designed to protect.

scholarly journals A Statistical Model for Predicting Neutropenic Fever

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 5258-5258
Author(s):  
Ariel Nelson ◽  
Dan Eastwood ◽  
Tao Wang ◽  
Karen Carlson ◽  
Laura C Michaelis ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Febrile Neutropenia ◽  
Predictive Value ◽  
Minimum Temperature ◽  
Temperature Data ◽  
Temperature Series ◽  
Series Data ◽  
Maximum Temperature ◽  
Current Definition ◽  
Single Temperature

Abstract Background Febrile neutropenia (FN) is a common occurrence associated with chemotherapy regimens used in patients (pts) with acute myelogenous leukemia (AML). Febrile neutropenia is presently defined as a single temperature of ≥38.3°C (101°F) or a temperature of ≥38.0°C (100.4°F) for >1 hour in a patient with an absolute neutrophil count < 500/mm3. Due to the potential for life threatening infections, fever in a patient with neutropenia is considered an oncologic emergency. Initiating appropriate antibiotic therapy as soon as possible in these patients leads to better outcomes. However, to our knowledge, there is no evidence that supports the current definition of neutropenic fever. Aim To identify a temperature pattern that is predictive of the subsequent development of febrile neutropenia in neutropenic pts with AML Methods After obtaining IRB approval we retrospectively obtained demographic and temperature data from hospitalized patients with AML undergoing induction therapy who were admitted to our institution between 12/8/2012 and 12/7/2013. Temperature data was recorded at intervals per physician order and nursing discretion during admission. We identified fever as a single temperature ≥38.3°C (101°F) or consecutive temperatures recorded 1 hour apart ≥38.06°C (100.5°F). Data was processed using SAS data programming to create and summarize this pilot temperature series data. Data for 68 patients containing 137 fever events was divided into 203 segments: a series was considered to end at the time of fever (or end of data) and a new series for the same patient began 24 hours after a preceding fever. Plots were created showing temperature over time leading up to fever (end of series). Our data consists of unequal interval time series data and does not lend itself to the usual methods of statistical ROC analysis. An ROC-like analysis to estimate sensitivity and specificity of a maximum temperature that predicts for a subsequent episode of FN was performed. Temperature data was subset into 7 time intervals: a pre-fever interval spanning 4 to 28 hours preceding fever or series end, and 6 non-fever intervals, each 24 hours long and spanning the period from 48 to 192 hours before fever or series end, for a total of 854 data windows. Statistics on each patient series within each interval were used as variables in predicting fever onset in logistic regression analysis. The variables included were maximum temperature within 24 hours, minimum temperature within 24 hours, average of positive increases between subsequent measurements, and largest 24 hour increase. Statistical analysis consisted of a generalized linear model with logit link (logistic regression) predicting fever at least 4 hours before onset, and used generalized estimating equations to adjust for correlated temperature measures within patient. Results Of the 68 patients identified, 47% were male, 53% were female with a mean age of 56.3 ± 15.1 years. Our fever curve plots suggest that there is an increase in average temperature at least 24 hours before the onset of fever in those patients that will go on to develop a fever by current definition (Figure 1). A prediction score including, maximum temperature within 24 hours, minimum temperature within 24 hours, average of positive increases between subsequent measurements, and largest 24 hour increase was able to predict 86.1% of oncoming FN events 4 to 28 hours before onset and reject 67.4% of non-FN events. This rule has a negative predictive value of 96.2% and a positive predictive value of 33.7%. Discussion Our analysis demonstrates the feasibility of using temperature series data for early prediction of FN. A more comprehensive analysis is planned and is expected to result in higher sensitivities. If subsequent analysis proves to be significant this data may be used to develop future prospective clinical studies to evaluate new fever criteria and may alter our current definition and management of pts with FN. Figure 1. 4-days of temperature series data preceding onset of fever or end of series if no fever. The dark lines are LOESS smoothed average temperatures for series ending in fever (dash) or non-fever (solid). Figure 1. 4-days of temperature series data preceding onset of fever or end of series if no fever. The dark lines are LOESS smoothed average temperatures for series ending in fever (dash) or non-fever (solid). Disclosures No relevant conflicts of interest to declare.

scholarly journals Simulating the Impact of Climate Change on Growth and Yield of Maize Using CERES-Maize Model under Temperate Kashmir

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Bilal Ahmad Lone ◽  
Shivam Tripathi ◽  
Asma Fayaz ◽  
Purshotam Singh ◽  
Sameera Qayoom ◽  
...  
Keyword(s):  
Climate Variability ◽  
Minimum Temperature ◽  
Maize Yield ◽  
Climate Impact ◽  
Growth And Yield ◽  
Weather Data ◽  
Maximum Temperature ◽  
The Impact ◽  
Global Food ◽  
Process Based Models

Climate variability has been and continues to be, the principal source of fluctuations in global food production in countries of the developing world and is of serious concern. Process-based models use simplified functions to express the interactions between crop growth and the major environmental factors that affect crops (i.e., climate, soils and management), and many have been used in climate impact assessments. Average of 10 years weather data from 1985 to 2010, maximum temperature shows an increasing trend ranges from 18.5 to 20.5°C.This means there is an increase of 2°C within a span of 25 years. Decreasing trend was observed with respect to precipitation was observed with the same data. The magnitude of decrease was from 925 mm to 650 mm of rainfall which is almost decrease of 275 mm of rainfall in 25 years. Future climate for 2011-2090 from A1B scenario extracted from PRECIS run shows that overall maximum and minimum temperature increase by 5.39°C (±1.76) and 5.08°C (±1.37) also precipitation will decrease by 3094.72 mm to 2578.53 (±422.12) The objective of this study was to investigate the effects of climate variability and change on maize growth and yield of Srinagar Kashmir. Two enhanced levels of temperature (maximum and minimum by 2 and 4°C) and CO2 enhanced by 100 ppm & 200 ppm were used in this study with total combinations of 9 with one normal condition.  Elevation of maximum and minimum temperature by 4°C anthesis  and maturity of maize was earlier 14 days with a deviation of 18%  and  26 days with a deviation  of 20% respectively. Increase in temperature by 2 to 4°C alone or in combination with enhanced levels of CO2 by 100 and 200 ppm the growth and yield of maize was drastically declined with an reduction of about 40% in grain yield. Alone enhancement of CO2  at both the levels fails show any significant impact on maize yield.

scholarly journals Solar energy density estimation using ANFIS based on daily maximum and minimum temperature

2019 ◽  
Vol 10 (4) ◽  
pp. 2206
Author(s):  
M. Irwanto ◽  
H. Alam ◽  
M. Masri ◽  
B. Ismail ◽  
W. Z. Leow ◽  
...  
Keyword(s):  
Energy Density ◽  
Solar Energy ◽  
Minimum Temperature ◽  
Fuzzy Inference ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Daily Maximum ◽  
Pv System ◽  
Application System ◽  
Inference System

The data of solar energy density in one area is very important when the area will constructed photovoltaic (PV) system. The data is as preliminary study to decide what the area is suitable or not to be constructed the PV application system. But, sometime the available data is missing because the limitation of weather equipment.  An alternative technique for the available data of solar energy density should be done for the continuity of PV application system decision. An estimation technique of solar energy density is one part of good alternative to solve this problem. This paper presents the estimation of solar energy density using Adaptive Neuron Fuzzy Inference System (ANFIS). The ANFIS system has two input data of the measured daily minimum, maximum temperature and difference between maximum and minimum temperature. The measured solar energy density is as target data of ANFIS system. The data is recorded from Medan meteorological station through the web site of world weather online for the year of 2018. The result shows that the average estimated solar energy density is classified in the very high solar energy density and based on the percentage error shows that the estimated solar energy density is acceptable.

scholarly journals Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Seema Patil ◽  
Sharnil Pandya
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Random Forest ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Support Vector ◽  
Forecasting Model ◽  
Random Forest Regression ◽  
Mean Wind Speed ◽  
Best Fit

For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R2) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.

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