scholarly journals Time Series Analysis of Mean Monthly Temperature in the Far Western Ethiopia, Assosa District

2020 ◽  
Author(s):  
Tigabu Hailu Kassa ◽  
Shewayiref Geremew Gebremichael
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Descriptive Statistics ◽  
Mean Annual Temperature ◽  
Temperature Pattern ◽  
First Order ◽  
Monthly Temperature ◽  
The Mean ◽  
One Year ◽  
Western Ethiopia

Abstract BackgroundThis study investigated the mean monthly temperature pattern of the Assosa district, Western Ethiopia. The objective of this study was to analyze the mean monthly temperature patterns in the Assosa district for the period from January 2012 to December 2016 based on data from meteorological stations in the Assosa district.MethodsDescriptive statistics and univariate Box-Jenkins methodology to build the seasonal ARIMA model were used.ResultsThe results showed that the mean annual temperature of Assosa was 28.025 degree Celsius. The original series was found to be seasonally non-stationary, as indicated by the ACF plot of the series. After using first-order seasonal differencing, the series was found to be stationary. A time-series model for the Assosa station was adjusted, processed, diagnostically checked, and finally, an ARIMA (3.0.1) model is established and this model is used to forecast one-year mean monthly temperature values. ConclusionThe forecasted mean temperature values showed a similar pattern to previous recordings.

Zooplankton Sample Variation on the Scotian Shelf

1978 ◽  
Vol 35 (9) ◽  
pp. 1207-1222 ◽  
Author(s):  
D. D. Sameoto
Keyword(s):  
Time Series ◽  
Fish Larvae ◽  
Geographic Area ◽  
Scotian Shelf ◽  
Tidal Cycles ◽  
Zooplankton Sample ◽  
Single Station ◽  
The Mean ◽  
One Year ◽  
Abundance And Distribution

Zooplankton sampling experiments on the Scotian Shelf during September 1973 and August 1974 using vertical tows demonstrated that numbers of many zooplankton species had a periodic fluctuation over 26 h. The fluctuations appeared related to the tide during 1974 but not in 1973. The periodic fluctuations accounted for a major portion of the sample variation in many species during both years. The mean numbers of small forms of zooplankton (copepods) obtained from the 26-h time series on a single station were very similar to means obtained during the same cruise over a wide area of the shelf. This suggested that a single station sampled over a period of two tidal cycles may be representative of the sample variation encountered over a wide geographic area of many hundreds of kilometres. Only Calanus and Pseudocalanus were correlated during all the experiments and from year to year. The abundance and distribution of the other species of zooplankton compared from one year to the next were not significantly correlated. This suggested the species populations were independent of one another. Key words: zooplankton, sample variation, time series, Scotian Shelf, fish larvae, Copepoda, tide

Methods for determining "reference changes" from serial measurements: plasma lipid-bound sialic acid.

1989 ◽  
Vol 35 (6) ◽  
pp. 972-974 ◽  
Author(s):  
S Shahangian ◽  
H A Fritsche ◽  
J I Hughes ◽  
D A Johnston
Keyword(s):  
Time Series ◽  
Sialic Acid ◽  
Healthy Subjects ◽  
Plasma Lipid ◽  
Autoregressive Time Series ◽  
The Mean ◽  
One Year ◽  
Mean Variance ◽  
Serial Measurements ◽  
Colorectal Adenocarcinomas

Abstract Lipid-bound sialic (neuraminic) acid (LSA) was measured in EDTA-treated plasma of 26 healthy subjects at three-month intervals for up to one year. The change in LSA concentration for consecutive measurements ranged from -54 to 42 mg/L (mean, -2.1 mg/L; SD, 19.6 mg/L; n = 56). The "reference change" for plasma LSA (+/- 2 SD), calculated from distribution of the differences, was +/- 39 mg/L. The 88th percentile of the intra-individual variance was 338 mg2/L2 and the mean variance was 159 mg2/L2. Using the homeostatic, autoregressive time-series model, a reference change of +/- 51 mg/L between two consecutive measurements was determined to be statistically significant (i.e., expected by chance no more than 5% of the time) in 88% of the healthy subjects. Only 73% of the healthy subjects would have had intra-individual variances corresponding to the reference change of +/- 39 mg/L according to the autoregressive model. The concentration of LSA in plasma was significantly decreased upon surgery in five of 10 patients with colorectal adenocarcinomas of Dukes stages A-C when we used +/- 39 mg/L as the reference change, but in only two of the 10 when we used +/- 51 mg/L as the reference change.

DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik

2020 ◽  
Vol 13 (02) ◽  
pp. 1-8
Author(s):  
Agrienvi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Production Data ◽  
Policy Makers ◽  
Strategic Value ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Arima Modeling

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

scholarly journals Selected agroclimatical characteristics of Žabčice region for period 1991–2005

2006 ◽  
Vol 54 (4) ◽  
pp. 81-90
Author(s):  
Jan Svoboda ◽  
Jan Brotan
Keyword(s):  
Absolute Maximum ◽  
Mean Annual Temperature ◽  
Measured Temperature ◽  
Absolute Minimum ◽  
Monthly Temperature ◽  
Temperature And Precipitation ◽  
Time Period ◽  
Normal Period ◽  
The Mean

Presented work is continuation of the first comprehensive agroclimatological study of Žabcice area (Rožnovský, Svoboda, 1995) and focuses on the time period 1991 to 2005. The work contains some of the agroclimatological data, which are used by number of experts at our university working within area of the MZLU Agricultural Farm in Žabčice especially on project no. J08/984321001. Results are based on data measured by the special agrometeorological station of Institute of Agrosystems and Bioclimatology at MZLU in Brno that is situated within trial area “Obora“ (altitude: 179 m; latitude: N 49°01´; longitude: E 16°16´). Measurements at Žabčice station follow methodology of Czech Hydrometeorological Institute (Slabá, 1972; Fišák, 1994).Climatic diagram according Walter and Leith is usually based on normal period (Rožnovský, Svoboda, 1995) but it is possible to prepare it for shorter period (in this case 1991–2005) and make a comparison. If the ratio of the temperature and precipitation axis is 10:30 (which is the most suitable according our experience) it may be stated that:• During period 1961–1990 the curve of precipitation sums is under the curve of mean monthly temperature from the middle of July till beginning of October. This period is the period of drought for Žabčice.• For the period from 1991 to 2005 the curve of precipitation sums is under the curve of temperature from mid April to mid June (just a slightly, but it can explain spring droughts appearances) and at the begining of August till the end of the first decade of September. In comparison with the normal pe- riod 1961–1990 and the long term mean 1901–1950 for station Židlochovice the probability of possible drought has increased.• Mean annual temperature changed from 9.2 °C to 10.0 °C and precipitation changed from 480.0 mm to 483.0 mm however with different distribution as can be seen at the fig. 4.• The mean monthly temperature of the coldest month increased from – 5.4 °C to – 3.9 °C and lowest measured temperature within this period was – 22.3 °C in comparison with 1961–1990 when the value was – 29.0 °C.• Mean monthly temperature of the warmest month increased from 25.2 °C to 27.2 °C and absolute maximum increased from 36.6 °C to 38.47 °C.• Months with the mean minimum monthly temperature below 0.0 °C and months with absolute minimum below 0.0 °C have also changed. All measured data and calculated values are shown in tables and figures.

scholarly journals S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Madhavi Latha Challa ◽  
Venkataramanaiah Malepati ◽  
Siva Nageswara Rao Kolusu
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Descriptive Statistics ◽  
Autoregressive Integrated Moving Average ◽  
Augmented Dickey Fuller ◽  
The Mean ◽  
Daily Stock Returns

AbstractThis study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

scholarly journals Time-variability of the fraction of young water in a small headwater catchment

2019 ◽  
Author(s):  
Michael P. Stockinger ◽  
Heye R. Bogena ◽  
Andreas Lücke ◽  
Christine Stumpp ◽  
Harry Vereecken
Keyword(s):  
Time Series ◽  
Time Window ◽  
High Sensitivity ◽  
Annual Runoff ◽  
Nutrient Loss ◽  
Time Variability ◽  
Short Term ◽  
Sampling Campaign ◽  
The Mean ◽  
One Year

Abstract. The time precipitation needs to travel through a catchment to its outlet is an important descriptor of a catchment's susceptibility to pollutant contamination, nutrient loss and hydrological functioning. The fast component of total water flow can be estimated by the fraction of young water (Fyw) which is the percentage of streamflow younger than three months. Fyw is calculated by comparing the amplitudes of sine waves fitted to seasonal precipitation and streamflow tracer signals. This is usually done for the complete tracer time series available neglecting annual differences in the amplitudes of longer time series. Considering inter-annual amplitude differences, we here employed a moving time window of one-year length in weekly time steps over a 4.5-years δ18O tracer time series to calculate 189 Fyw results. The results were then tested against the following null hypotheses, defining 2 % difference in Fyw as significant based on results of previous studies: (1) Fyw does not deviate more than ±2% from the mean of all Fyw results indicating long-term invariance. Larger deviations would indicate either flow path changes or a change in the relative contribution of different flow paths; (2) for any four-week window Fyw does not change more than ±2 % indicating short-term invariance. Larger deviations would indicate a high sensitivity of Fyw to a 1–4 weeks shift in the start of a one-year sampling campaign; (3) for a given calendar month Fyw does not change more than ±2 % indicating seasonal invariance of Fyw. In our study, all three null hypotheses were rejected. Thus, the Fyw results were time-variable, showed a high variability in the chosen sampling time and had no pronounced seasonality. Based on high short-term variability of Fyw when the mean adjusted R² was below 0.2 we recommend that a low R2 should be regarded as indicating potentially highly uncertain Fyw results. Furthermore, while investigated individual meteorological factors could not sufficiently explain variations of Fyw, the runoff coefficient showed a moderate negative correlation of r = −0.54 with Fyw. This indicated that when annual runoff exceeded precipitation the catchment received the water deficit from storage which is old water causing a decrease in Fyw. The results of this study suggest that care must be taken when comparing Fyw of catchments that were based on different calculation time periods.

scholarly journals Time series and trend analysis of brucellosis in Oskou county, EastAzerbaijan: 2007-2016

2019 ◽  
Vol 9 (4) ◽  
pp. 285-290
Author(s):  
Hosein Rafiemanesh ◽  
Yousef Alimohamadi ◽  
Seyed Rasoul Hashemi Aghdam ◽  
Avaz Safarzadeh ◽  
Abolghasem Shokri ◽  
...  
Keyword(s):  
Time Series ◽  
Rural Areas ◽  
Urban Areas ◽  
Arima Model ◽  
Cross Sectional Study ◽  
Human Brucellosis ◽  
Cross Sectional ◽  
Rural And Urban Areas ◽  
The Mean ◽  
Incident Cases

Background: The epidemiology of human brucellosis has drastically changed in recent years. This study aims to assess trend in brucellosis in the Oskou county, East Azerbaijan, Iran. Methods: This cross-sectional study was conducted on all confirmed brucellosis cases over the period between 2007 and 2016 in Oskuo county. We use crude incidence rate (CIR) per100000 persons and carried out Joinpoint regression analysis to describe brucellosis trend over the study period. Also, we used ARIMA model to predict trend and number of new brucellosis cases for the coming years. Results: More than 90% (92.5%; 95% CI: 89.9-95.1) of brucellosis cases were in rural areas over the study period. In recorded cases, 60.5% (95% CI: 55.6-65.4) of total cases were men and 39.5% (95% CI: 34.6-44.4) of total cases were women. The mean age of men was 33.85(SD=19.72) years and the mean age of women was 35.88 (SD=17.26) years old. Majority of brucellosis cases occurred in spring. CIRs for the rural and urban areas were 47.62 to132.20 and zero to 18.55, respectively. The CIR for rural area had decreasing trend to 2011 and increasing for 2011-2017. Conclusion: Based-on time series analysis, the number of new cases in the future years has fixed trend and the most number of incident cases will be occurred between third to fifth months in each years.

scholarly journals Fuzzy First-Order Transition-Rules-Trained Hybrid Forecasting System for Short-Term Wind Speed Forecasts

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3332 ◽  
Author(s):  
Yulong Bai ◽  
Lihong Tang ◽  
Manhong Fan ◽  
Xiaoyan Ma ◽  
Yang Yang
Keyword(s):  
Neural Network ◽  
Hybrid System ◽  
Arima Model ◽  
Order Transition ◽  
First Order ◽  
The Neural Network ◽  
In Series ◽  
The Mean ◽  
First Order Transition ◽  
Transition Rules

Due to the ever-increasing environmental pollution becoming progressively more serious, wind power has been widely used around the world in recent years. However, because of their randomness and intermittence, the accurate prediction of wind speeds is difficult. To address this problem, this article proposes a hybrid system for short-wind-speed prediction. The system combines the autoregressive differential moving average (ARIMA) model with a three-layer feedforward neural network. An ARIMA model was employed to predict linear patterns in series, while a feedforward neural network was used to predict the nonlinear patterns in series. To improve accuracy of the predictions, the neural network models were trained by using two methods: first-order transition rules and fuzzy first-order transition rules. The Levenberg–Marquardt (LM) algorithm was applied to update the weight and deviation of each layer of neural network. The dominance matrix method was employed to calculate the weight of the hybrid system, which was used to establish the linear hybrid system. To evaluate the performance, three statistical indices were used: the mean square error (MSE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE). A set of Lorenz-63 simulated values and two datasets collected from different wind fields in Qilian County, Qinghai Province, China, were utilized as to perform a comparative study. The results show the following: (a) compared with the neural network trained by first-order transition rules, the prediction accuracy of the neural network trained by the fuzzy first-order transition rules was higher; (b) the proposed hybrid system attains superior performance compared with a single model; and (c) the proposed hybrid system balances the forecast accuracy and convergence speed simultaneously during forecasting. Therefore, it was feasible to apply the hybrid model to the prediction of real time-series.

scholarly journals A flexible univariate moving average time-series model for dispersed count data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kimberly F. Sellers ◽  
Ali Arab ◽  
Sean Melville ◽  
Fanyu Cui
Keyword(s):  
Time Series ◽  
Count Data ◽  
Time Series Data ◽  
Moving Average ◽  
Simulated Data ◽  
Series Data ◽  
First Order ◽  
Moving Average Model ◽  
The Mean ◽  
Special Case

AbstractAl-Osh and Alzaid (1988) consider a Poisson moving average (PMA) model to describe the relation among integer-valued time series data; this model, however, is constrained by the underlying equi-dispersion assumption for count data (i.e., that the variance and the mean equal). This work instead introduces a flexible integer-valued moving average model for count data that contain over- or under-dispersion via the Conway-Maxwell-Poisson (CMP) distribution and related distributions. This first-order sum-of-Conway-Maxwell-Poissons moving average (SCMPMA(1)) model offers a generalizable construct that includes the PMA (among others) as a special case. We highlight the SCMPMA model properties and illustrate its flexibility via simulated data examples.

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