Models For Forecasting The Number Of Russian Grandparents

2021 ◽  
Author(s):  
Anna Bagirova ◽  
Oksana Shubat
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Statistical Characteristics ◽  
Trend Model ◽  
Human Fertility ◽  
First Child ◽  
Absolute Growth ◽  
Statistical Survey ◽  
Age Of Entry ◽  
Logistic Functions

Russian demographic statistics does not provide information about the number of grandparents. The aim of our study is to present models for forecasting their number. We used data from the Human Fertility Database to estimate the average age of a mother at the birth of her first child. Based on the simulated age of Russian women’s entry into grandparenthood, the time series of the number of Russian grandmothers was created. To obtain prospective estimates of the number of Russian grandmothers, we tested various models used in demography to forecast population size – mathematical (based on exponential and logistic functions) and statistical (based on statistical characteristics of time series). To estimate the number of grandmothers who are significantly involved in caring for grandchildren, we used data from the Federal statistical survey. Our results are as follows: 1) there is an increase in the age of entry into grandparenthood; 2) we estimated the size of potential grandmothers in different years and we found two models which are more appropriate for forecasting: linear trend model and average absolute growth model; 3) using these models, we predicted an increase in the number of both potential and active grandmothers in the next 5 years.

scholarly journals Growth Model and Forecasting Prices of Some Agricultural Products in Bangladesh

2021 ◽  
Vol 26 (2) ◽  
pp. 64-71
Author(s):  
Md Hossain
Keyword(s):  
Time Series ◽  
Growth Model ◽  
Selection Criteria ◽  
Time Series Data ◽  
Linear Trend ◽  
Series Data ◽  
Percentage Error ◽  
Absolute Deviation ◽  
Trend Model ◽  
Exponential Method

The aim of this paper was to explore the appropriate deterministic time series model using the latest selection criteria considering the price pattern of onion, garlic and potato products in Bangladesh (January 2000 to December 2016). It appeared from our analysis that the time series data for the prices of potato was first order homogenous stationary but onion and garlic were found to be the second order stationary. Four different forecasting models namely, linear trend model, quadratic trend model, exponential growth model, and S-curve trend model were used to find the best fitted model for the prices of above mentioned products in the Bangladesh. Three accuracy measures such as mean absolute percentage error (MAPE), mean absolute deviation (MAD) and mean squared deviation (MSD) were used for the selection of the best fitted model based on lowest value of forecasting error. Lowest values of these errors indicated a best fitted model. After choosing the best growth model by the latest model selection criteria, prices of selected agricultural commodities were forecasted using the following time-series analysis methods: Simple Exponential Method, Double Exponential Method using the time period from January 2017 to December 2021. The findings of this study would be useful for policy makers, researchers, businessmen as well as producers in order to forecast future prices of these commodities.

Statistical evaluation of temporary changes in annual precipitation in the plain territory of Ukraine

2020 ◽  
Vol 101-102 (3-4) ◽  
pp. 7-18
Author(s):  
Liudmyla Palamarchuk ◽  
Iryna Shedemenko
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Statistical Characteristics ◽  
Annual Precipitation ◽  
Mean Annual Precipitation ◽  
Positive Trend ◽  
The South ◽  
Positive Skewness ◽  
Weather Stations ◽  
Skewness And Kurtosis

The field of precipitation of the plain territory of Ukraine is investigated according to the data of evenly spaced 18 weather stations. The annual precipitation is analyzed for periods of different duration (from the beginning of observations at the station until 2015) and for the period 1961-2015. The main statistical characteristics are calculated, the patterns of their changes in the study area are shown. Gradient of decrease in multi-year annual precipitation for 1961-2015 (650 to 400 mm) directed from the northwest to the south and southeast of the country. The value of positive skewness and kurtosis, the coefficient of variation (0.16-0.26), on the contrary, increases in this direction. The standard deviation (91-137 mm) is maximum in the southwest and in the center of the plain part of Ukraine. It was determined that the distribution of annual precipitation can be considered normal, mainly with positive skewness and kurtosis. Multi-year fluctuations in annual precipitation are approximated by linear trend equations and a polynomial of the 6th degree. Regions with a negative and positive linear trend of annual precipitation in 1961-2015 were identified. A downward trend in precipitation was noted at stations located in a “strip” from the southwest (Chernivtsi) to the northeast (Sumy) through the center of Ukraine. In the south-west of this region (Vinnytsia), the decrease in precipitation is the greatest: the negative linear trend is statistically significant, the slope of the trend is -2.35, the coefficient of determination is 0.14; mean annual precipitation for 1991-2015 compared to 1961-1990 less by 10.5%, 53.4 mm. In the rest of the plain territory of the country, there was a tendency towards an increase in precipitation, but the positive trend for all stations is statistically insignificant. The absence of statistically significant linear trends (except for Vinnytsia) can be explained by the relative stability of the multi-year precipitation regime during this period. The use of a more complex approximation and a long time series of observations increased the trend approximation confidence, but the influence of these factors is not unambiguous for all weather stations. On the graphs of polynomial trends, the cycle manifestation in the time series of annual precipitation depends on the length of the observation series and decreases from west to east of Ukraine. The duration of the cycles is 25-30 and 35-40 years when determined according to the data of 1961-2015, and from 70 to 90 and 120 years according to the series of observations more than 100 years. In 2016-2025, as shown by estimates by the equations of polynomials of the 6th degree, a decrease in annual precipitation will prevail on the plain territory of Ukraine compared to 1961-2015. The largest decrease (by 10-13%) is likely in the central regions (Poltava, Dnipro). an increase (by 5%) - in the southwest (Vinnitsa, Chernivtsi).

scholarly journals Stochastic modeling of Lake Van water level time series with jumps and multiple trends

2013 ◽  
Vol 17 (6) ◽  
pp. 2297-2303 ◽  
Author(s):  
H. Aksoy ◽  
N. E. Unal ◽  
E. Eris ◽  
M. I. Yuce
Keyword(s):  
Time Series ◽  
Water Level ◽  
Lake Water ◽  
Linear Trend ◽  
Lake Van ◽  
Lake Water Level ◽  
Trend Model ◽  
Level Data ◽  
Water Level Data ◽  
Surrounding Areas

Abstract. In the 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey, has risen up about 2 m. Analysis of the hydrometeorological data shows that change in the water level is related to the water budget of the lake. In this study, stochastic models are proposed for simulating monthly water level data. Two models considering mono- and multiple-trend time series are developed. The models are derived after removal of trend and periodicity in the dataset. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. In the so-called mono-trend model, the time series is treated as a whole under the hypothesis that the lake water level has an increasing trend. In the second model (so-called multiple-trend), the time series is divided into a number of segments to each a linear trend can be fitted separately. Application on the lake water level data shows that four segments, each fitted with a trend line, are meaningful. Both the mono- and multiple-trend models are used for simulation of synthetic lake water level time series under the hypothesis that the observed mono- and multiple-trend structure of the lake water level persist during the simulation period. The multiple-trend model is found better for planning the future infrastructural projects in surrounding areas of the lake as it generates higher maxima for the simulated lake water level.

scholarly journals FORECASTING OF AREA AND PRODUCTION OF CASHEW NUT IN DAKSHINA KANNADA USING ARIMA AND EXPONENTIAL SMOOTHING MODELS

2019 ◽  
pp. 61-76
Author(s):  
Chaithra M., ◽  
Pramit Pandit ◽  
Bishvajit Bakshi
Keyword(s):  
Time Series ◽  
Linear Trend ◽  
Work Force ◽  
Exponential Smoothing ◽  
Vital Role ◽  
Time Series Forecasting ◽  
Trend Model ◽  
Forecasting Models ◽  
Cashew Nut ◽  
Nut Production

The cultivation and marketing of cashew nut involve a considerable amount of work force. Hence, it plays a vital role in the Indian economic scenario. In this context, an attempt has been made to forecast the area and production of cashew nut with a view to help the planners in recommending policies regarding cashew nut. Due to autocorrelation in the data, time series forecasting models such as ARIMA and exponential smoothing models were adopted. Detection and removal of 3 significant outliers, i.e. 1 for area under cashew nut and 2 in case of cashew nut production, were done before fitting the models. Holt’s model was found to have better forecasting ability with lowest RMSE value (1386.13) among the different models fitted for forecasting the area under cashew nut. From this model, area (ha) under cashew nut was forecasted to be 34492.10, 34974.81 and 35474.87 for the year 2018, 2019 and 2020, respectively. In case of cashew nut production, Brown’s linear trend model, with RMSE value (10020.19), was observed to have better forecasting ability among the tried models. Production of cashew nut (in tonnes) was forecasted to be 10230.20, 10996.81 and 11833.00 for the year 2018, 2019 and 2020, respectively.  

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

scholarly journals Benchmarking homogenization algorithms for monthly data

2012 ◽  
Vol 8 (1) ◽  
pp. 89-115 ◽  
Author(s):  
V. K. C. Venema ◽  
O. Mestre ◽  
E. Aguilar ◽  
I. Auer ◽  
J. A. Guijarro ◽  
...  
Keyword(s):  
Time Series ◽  
Performance Metrics ◽  
Linear Trend ◽  
Simulated Data ◽  
Integrated Approach ◽  
Global Network ◽  
Temperature And Precipitation ◽  
Skill Scores ◽  
Simulated Network ◽  
The Individual

Abstract. The COST (European Cooperation in Science and Technology) Action ES0601: advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies and because they represent two important types of statistics (additive and multiplicative). The algorithms were validated against a realistic benchmark dataset. The benchmark contains real inhomogeneous data as well as simulated data with inserted inhomogeneities. Random independent break-type inhomogeneities with normally distributed breakpoint sizes were added to the simulated datasets. To approximate real world conditions, breaks were introduced that occur simultaneously in multiple station series within a simulated network of station data. The simulated time series also contained outliers, missing data periods and local station trends. Further, a stochastic nonlinear global (network-wide) trend was added. Participants provided 25 separate homogenized contributions as part of the blind study. After the deadline at which details of the imposed inhomogeneities were revealed, 22 additional solutions were submitted. These homogenized datasets were assessed by a number of performance metrics including (i) the centered root mean square error relative to the true homogeneous value at various averaging scales, (ii) the error in linear trend estimates and (iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Contingency scores by themselves are not very informative. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Training the users on homogenization software was found to be very important. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that automatic algorithms can perform as well as manual ones.

scholarly journals On the Connection between the GEP Performances and the Time Series Properties

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1853
Author(s):  
Alina Bărbulescu ◽  
Cristian Ștefan Dumitriu
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Functional Form ◽  
Financial Time Series ◽  
Gene Expression Programming ◽  
Parametric Models ◽  
Statistical Characteristics ◽  
Data Series ◽  
Financial Time ◽  
Data Generating Process

Artificial intelligence (AI) methods are interesting alternatives to classical approaches for modeling financial time series since they relax the assumptions imposed on the data generating process by the parametric models and do not impose any constraint on the model’s functional form. Even if many studies employed these techniques for modeling financial time series, the connection of the models’ performances with the statistical characteristics of the data series has not yet been investigated. Therefore, this research aims to study the performances of Gene Expression Programming (GEP) for modeling monthly and weekly financial series that present trend and/or seasonality and after the removal of each component. It is shown that series normality and homoskedasticity do not influence the models’ quality. The trend removal increases the models’ performance, whereas the seasonality elimination results in diminishing the goodness of fit. Comparisons with ARIMA models built are also provided.

scholarly journals Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia

2012 ◽  
Vol 19 (6) ◽  
pp. 675-683 ◽  
Author(s):  
K. Moghtased-Azar ◽  
A. Mirzaei ◽  
H. R. Nankali ◽  
F. Tavakoli
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Salt Lake ◽  
Linear Trend ◽  
Groundwater Resources ◽  
Time Interval ◽  
Gps Measurements ◽  
Lake Urmia ◽  
North West ◽  
Continuous Gps

Abstract. Lake Urmia, a salt lake in the north-west of Iran, plays a valuable role in the environment, wildlife and economy of Iran and the region, but now faces great challenges for survival. The Lake is in immediate and great danger and is rapidly going to become barren desert. As a result, the increasing demands upon groundwater resources due to expanding metropolitan and agricultural areas are a serious challenge in the surrounding regions of Lake Urmia. The continuous GPS measurements around the lake illustrate significant subsidence rate between 2005 and 2009. The objective of this study was to detect and specify the non-linear correlation of land subsidence and temperature activities in the region from 2005 to 2009. For this purpose, the cross wavelet transform (XWT) was carried out between the two types of time series, namely vertical components of GPS measurements and daily temperature time series. The significant common patterns are illustrated in the high period bands from 180–218 days band (~6–7 months) from September 2007 to February 2009. Consequently, the satellite altimetry data confirmed that the maximum rate of linear trend of water variation in the lake from 2005 to 2009, is associated with time interval from September 2007 to February 2009. This event was detected by XWT as a critical interval to be holding the strong correlation between the land subsidence phenomena and surface temperature. Eventually the analysis can be used for modeling and prediction purposes and probably stave off the damage from subsidence phenomena.

scholarly journals Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species

2019 ◽  
Author(s):  
David D. Parrish ◽  
Richard G. Derwent ◽  
Simon O'Doherty ◽  
Peter G. Simmonds
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Linear Trend ◽  
Power Series Expansion ◽  
Mathematical Representation ◽  
Seasonal Cycles ◽  
Data Sets ◽  
Significant Information ◽  
Long Term Changes

Abstract. We present an approach to derive a systematic mathematical representation of the statistically significant features of the average long-term changes and seasonal cycle of concentrations of trace tropospheric species. The results for two illustrative data sets (time series of baseline concentrations of ozone and N2O at Mace Head, Ireland) indicate that a limited set of seven or eight parameter values provides this mathematical representation for both example species. This method utilizes a power series expansion to extract more information regarding the long-term changes than can be provided by oft-employed linear trend analyses. In contrast, the quantification of average seasonal cycles utilizes a Fourier series analysis that provides less detailed seasonal cycles than are sometimes represented as twelve monthly means; including that many parameters in the seasonal cycle representation is not usually statistically justified, and thereby adds unnecessary noise to the representation and prevents a clear analysis of the statistical uncertainty of the results. The approach presented here is intended to maximize the statistically significant information extracted from analyses of time series of concentrations of tropospheric species regarding their mean long-term changes and seasonal cycles, including non-linear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

scholarly journals Time Series Analysis of the Pollutants Imissions in Urban Areas

1970 ◽  
Vol 66 (2) ◽  
Author(s):  
Carmen Leane NICOLESCU ◽  
Daniel DUNEA ◽  
Virgil MOISE ◽  
Gabriel GORGHIU
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Urban Areas ◽  
Linear Trend ◽  
Moving Average ◽  
Quality Parameters ◽  
Temporal And Spatial Distribution ◽  
Simple Exponential Smoothing ◽  
Environmental Air ◽  
Local Agencies

Environmental pollution of urban areas is one of the key factors that local agencies and authorities have to consider in the decision-making process. To succeed a sustainable management of the environment, there is necessary to use different kinds of instruments in order to evaluate and forecast the evolution of the environmental state. Understanding temporal and spatial distribution of air quality is essential in making decisions for regional management. In this paper a model for urban air quality forecasting using time series of monthly averages concentrations is presented. Sedimentable dusts (SD), total suspended particulates (TSP), nitrogen dioxide (NO2), and sulfur dioxide (SO2), imissions, recorded between 1995 and 2008 in the urban area of Târgovişte city are used as inputs in the model. The measured pollutant data from the local Environmental Agency database were statistically analyzed in time series including monthly patterns using the auto-regressive integrated moving average (ARIMA) method, linear trend, simple moving average of three terms and simple exponential smoothing. There was discussed the efficiency of using this method in forecasting the environmental air quality. In general, ARIMA technique scores well in predicting the analysed environmental air quality parameters.

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