scholarly journals Time Series Analysis of Trend and Variability of Monthly Total Rainfall

2021 ◽  
Vol 10 (2) ◽  
pp. 104-114
Author(s):  
R. Ramakrishna ◽  
◽  
R.Gautham Goud ◽  
Alemayehu Sbhat ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Descriptive Statistics ◽  
Total Rainfall ◽  
Tigray Region ◽  
Augmented Dickey Fuller ◽  
Monthly Total ◽  
Accumulated Rainfall ◽  
History Of

The long-term variation in rainfall, one of the most important conditions for the climate in a particular region. The purpose of this study was to analysis the total monthly rainfall in the Maychew, which is located in the Tigray region of Ethiopia. The monthly rainfall is on the Maychew meteorological station has been calculated for the period from 2007-2018. The data were analyzed with the help of Minitab-14, R-3.3.1 an Overview of the descriptive statistics and unvaried, Box-Jenkins method, The seasonal ARIMA model was built to analyze the observed data and forecast the total rainfall, after the detection of nonstationarity using the Augmented Dickey-Fuller Test is a Test, and time plot. Some of the main findings of the study indicated that the monthly total rainfall tends to increase. In addition, it was found that, on the basis of the data contained in the history of the last twelve years of age. In addition, the descriptive statistics show that the average amount of rainfall in the Maychew is 58.82. After non-seasonal the first-order differentiation and once seasonal series, differentiation, they will be moved. A time series model for the Maychew Station and was adapted to be processed, diagnostically tested, and ultimately, to be obtained by SARIMA (3, 2, 2)*(0, 2, 2)12 a model has been created, and this model was used to Forecast the two years monthly values of the total rainfall. The forecasted accumulated rainfall values showed a similar pattern to the previous reports.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

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 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.

scholarly journals Forecasting mean monthly maximum and minimum air temperature of Jalandhar district of Punjab, India using seasonal ARIMA model

2022 ◽  
Vol 24 (1) ◽  
Author(s):  
BALJEET KAUR ◽  
NAVNEET KAUR ◽  
K. K. GILL ◽  
JAGJEEVAN SINGH ◽  
S. C. BHAN ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Goodness Of Fit ◽  
Time Series Data ◽  
Arima Model ◽  
Kendall Test ◽  
Series Data ◽  
Data Series ◽  
Augmented Dickey Fuller ◽  
Minimum Air Temperature

The long-term air temperature data series from 1971-2019 was analyzed and used for forecasting mean monthly air temperature at the district level. The Augmented Dickey-Fuller test, Kwiatkowski-Phillips-Schmidt-Shin test, and Mann-Kendall test were employed to test the stationarity and trend of the time series. The mean monthly maximum air temperature did not show any significant variation while an increasing trend of 0.04°C per annum was observed in mean monthly minimum air temperature, which was detrended. Box-Jenkins autoregressive integrated moving–averages were used to forecast the forthcoming 5 years (2020-2024) air temperature in the district Jalandhar of Punjab. The goodness of fit was tested against residuals, the autocorrelation function, and the histogram. The fitted model was able to capture dynamics of the time series data and produce a sensible forecast.

Forecasting of Cotton Production in India using Advanced Time Series Models

2020 ◽  
pp. 583-590
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Data Series ◽  
Time Series Models ◽  
Annual Data ◽  
Cotton Production ◽  
Out Of Sample ◽  
Augmented Dickey Fuller ◽  
Logarithmic Series ◽  
Best Fit

Reliable and timely estimates of cotton production are important providing useful inputs to policymakers for proper foresighted and informed planning. So an attempt was made to forecast the production of cotton at all India level using a time series model. The annual data on production of cotton for the period 1951-52 to 2018-19 was processed. The data were transformed into logarithmic series to stabilize the variance of the series. The stationarity of the data was checked with the help of the Augmented Dickey-Fuller and Phillips-Perron tests. The results of ADF and PP tests confirmed the cotton production series was non-stationary at level, so stationarity in the data was brought by differencing the data series at a first lag. The pattern present in ACF and PACF and results of SCAN and ESACF provided guideline to select the order of non-seasonal ARIMA model. The best fit ARIMA model (ARIMA: 3 1 1) was selected based on AIC criteria and residual diagnostic. The performance of the model was judged based on the MAPE value. The out of sample forecast of cotton production at all India level was carried out for the period 2019-20 to 2021-22. The forecasted values indicated a slight increase in the production of cotton compared to 2018-19.

scholarly journals Box-Jenkins ARIMA Modelling: Forecasting FDI in India

2020 ◽  
Author(s):  
Deepanshu Sharma ◽  
Kritika Phulli
Keyword(s):  
Time Series ◽  
Foreign Direct Investment ◽  
Steady State ◽  
Direct Investment ◽  
Arima Model ◽  
Model Generation ◽  
Augmented Dickey Fuller ◽  
Optimal Forecasting ◽  
Adf Test ◽  
Stationary Form

In the rapidly advancing dynamics of the economy trends of countries, the forecasting econometric techniques hold significant importance in the field of advance economics and management. Thus, this study intends to create Box Jenkins time series ARIMA model for analysing and predicting the trend of net FDI (Foreign Direct Investment) in India. The model was generated on the dataset of FDI inflow of India from the year 1950 to 2020. The trend was analysed for the generation of the model that best fitted the forecasting. The study highlights the minimum AIC value and involves ADF test (Augmented Dickey-Fuller) to transform FDI data into stationary form for model generation. It proposes ARIMA (1,1,4) model for optimal forecasting of net FDI inflow in India with an accuracy of 96.5%. The model thus predicts the steady-state exponential growth of FDI inflow in the coming 2020-25.In the rapidly advancing dynamics of the economy trends of countries, the forecasting econometric techniques hold significant importance in the field of advance economics and management. Thus, this study intends to create Box Jenkins time series ARIMA model for analysing and predicting the trend of net FDI (Foreign Direct Investment) in India. The model was generated on the dataset of FDI inflow of India from the year 1950 to 2020. The trend was analysed for the generation of the model that best fitted the forecasting. The study highlights the minimum AIC value and involves ADF test (Augmented Dickey-Fuller) to transform FDI data into stationary form for model generation. It proposes ARIMA (1,1,4) model for optimal forecasting of net FDI inflow in India with an accuracy of 96.5%. The model thus predicts the steady-state exponential growth of FDI inflow in the coming 2020-25.

scholarly journals Rainfall Trends and Malaria Occurrences in Limpopo Province, South Africa

2019 ◽  
Vol 16 (24) ◽  
pp. 5156 ◽  
Author(s):  
Abiodun Adeola ◽  
Katlego Ncongwane ◽  
Gbenga Abiodun ◽  
Thabo Makgoale ◽  
Hannes Rautenbach ◽  
...  
Keyword(s):  
Time Series ◽  
Malaria Transmission ◽  
Daily Rainfall ◽  
Limpopo Province ◽  
P Value ◽  
Total Rainfall ◽  
Rainfall Trends ◽  
Monthly Total ◽  
Vhembe District ◽  
Spearman’S Correlation

This contribution aims to investigate the influence of monthly total rainfall variations on malaria transmission in the Limpopo Province. For this purpose, monthly total rainfall was interpolated from daily rainfall data from weather stations. Annual and seasonal trends, as well as cross-correlation analyses, were performed on time series of monthly total rainfall and monthly malaria cases in five districts of Limpopo Province for the period of 1998 to 2017. The time series analysis indicated that an average of 629.5 mm of rainfall was received over the period of study. The rainfall has an annual variation of about 0.46%. Rainfall amount varied within the five districts, with the northeastern part receiving more rainfall. Spearman’s correlation analysis indicated that the total monthly rainfall with one to two months lagged effect is significant in malaria transmission across all the districts. The strongest correlation was noticed in Vhembe (r = 0.54; p-value = <0.001), Mopani (r = 0.53; p-value = <0.001), Waterberg (r = 0.40; p-value =< 0.001), Capricorn (r = 0.37; p-value = <0.001) and lowest in Sekhukhune (r = 0.36; p-value = <0.001). Seasonally, the results indicated that about 68% variation in malaria cases in summer—December, January, and February (DJF)—can be explained by spring—September, October, and November (SON)—rainfall in Vhembe district. Both annual and seasonal analyses indicated that there is variation in the effect of rainfall on malaria across the districts and it is seasonally dependent. Understanding the dynamics of climatic variables annually and seasonally is essential in providing answers to malaria transmission among other factors, particularly with respect to the abrupt spikes of the disease in the province.

scholarly journals Seasonal Autoregressive Integrated Moving Average (SARIMA) Model for the Analysis of Frequency of Monthly Rainfall in Osun State, Nigeria

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Samuel Olorunfemi Adams ◽  
Bello Mustapha ◽  
Auta Irinew Alumbugu
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Original Data ◽  
Monthly Rainfall ◽  
Time Series Modeling ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Augmented Dickey Fuller ◽  
Auto Regressive ◽  
Best Fit

The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is proposed for Osun State monthly rainfall data and the analysis was based on probability time series modeling approach. The Plot of the original data shows that the time series is stationary and the Augmented Dickey-Fuller test did not suggest otherwise. The graph further displays evidence of seasonality and it was removed by seasonal differencing. The plots of the ACF and PACF show spikes at seasonal lags respectively, suggesting SARIMA (1, 0, 1) (2, 1, 1). Though the diagnostic check on the model favoured the fitted model, the Auto Regressive parameter was found to be statistically insignificant and this led to a reduced SARIMA (1, 0, 1) (1, 1, 1)  model that best fit the data and was used to make forecast.

Forecasting based on spectral time series analysis: prediction of the Aurubis stock price

2020 ◽  
Vol 17 (4) ◽  
pp. 215-227
Author(s):  
Julia Babirath ◽  
Karel Malec ◽  
Rainer Schmitl ◽  
Kamil Maitah ◽  
Mansoor Maitah
Keyword(s):  
Time Series ◽  
Risk Management ◽  
Time Series Analysis ◽  
Wiener Process ◽  
Stock Price ◽  
Percentage Error ◽  
Portfolio Performance ◽  
Stock Price Prediction ◽  
Series Analysis ◽  
Augmented Dickey Fuller

The attempt to predict stock price movements has occupied investors ever since. Reliable forecasts are a basis for investment management, and improved forecasting results lead to enhanced portfolio performance and sound risk management. While forecasting using the Wiener process has received great attention in the literature, spectral time series analysis has been disregarded in this respect. The paper’s main objective is to evaluate whether spectral time series analysis can produce reliable forecasts of the Aurubis stock price. Aurubis poses a suitable candidate for an investor’s portfolio due to its sound economic and financial situation and the steady dividend policy. Additionally, reliable management contributes to making Aurubis an investment opportunity. To judge if the achieved forecast results can be considered satisfactory, they are compared against the simulation results of a Wiener process. After de-trending the time series using an Augmented Dickey-Fuller test, the residuals were compartmentalized into sine and cosine functions. The frequencies, amplitude, and phase were obtained using the Fast Fourier transform. The mean absolute percentage error measured the accuracy of the stock price prediction, and the results showed that the spectral analysis was able to deliver superior results when comparing the simulation using a Wiener process. Hence, spectral time series can enhance stock price forecasts and consequently improve risk management.

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