scholarly journals SARIMA Modelling and Forecasting of Monthly Rainfall Patterns for Coimbatore, Tamil Nadu, India

2020 ◽  
pp. 69-76
Author(s):  
S. Kokilavani ◽  
R. Pangayarselvi ◽  
S. P. Ramanathan ◽  
Ga. Dheebakaran ◽  
N. K. Sathyamoorthy ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Tamil Nadu ◽  
Weather Forecasting ◽  
Moving Average ◽  
Model Identification ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Diagnostic Checking ◽  
Partial Autocorrelation Function ◽  
Rainfall Patterns

Weather forecasting is an important subject in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. The present paper describes an empirical study for modeling and forecasting the time series of monthly rainfall patterns for Coimbatore, Tamil Nadu. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The best SARIMA models were selected based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) and the minimum values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The study has shown that the SARIMA (0,0,0)(2,0,0)12 model was appropriate for analysing and forecasting the future rainfall patterns. The Root Means Square Error (RMSE) values were found to be 52.37 and proved that the above model was the best model for further forecasting the rainfall.

scholarly journals Self-Identification ResNet-ARIMA Forecasting Model

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Model Identification ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Partial Autocorrelation Function

The challenging endeavor of a time series forecast model is to predict the future time series data accurately. Traditionally, the fundamental forecasting model in time series analysis is the autoregressive integrated moving average model or the ARIMA model requiring a model identification of a three-component vector which are the autoregressive order, the differencing order, and the moving average order before fitting coefficients of the model via the Box-Jenkins method. A model identification is analyzed via the sample autocorrelation function and the sample partial autocorrelation function which are effective tools for identifying the ARMA order but it is quite difficult for analysts. Even though a likelihood based-method is presented to automate this process by varying the ARIMA order and choosing the best one with the smallest criteria, such as Akaike information criterion. Nevertheless the obtained ARIMA model may not pass the residual diagnostic test. This paper presents the residual neural network model, called the self-identification ResNet-ARIMA order model to automatically learn the ARIMA order from known ARIMA time series data via sample autocorrelation function, the sample partial autocorrelation function and differencing time series images. In this work, the training time series data are randomly simulated and checked for stationary and invertibility properties before they are used. The result order from the model is used to generate and fit the ARIMA model by the Box-Jenkins method for predicting future values. The whole process of the forecasting time series algorithm is called the self-identification ResNet-ARIMA algorithm. The performance of the residual neural network model is evaluated by Precision, Recall and F1-score and is compared with the likelihood basedmethod and ResNET50. In addition, the performance of the forecasting time series algorithm is applied to the real world datasets to ensure the reliability by mean absolute percentage error, symmetric mean absolute percentage error, mean absolute error and root mean square error and this algorithm is confirmed with the residual diagnostic checks by the Ljung-Box test. From the experimental results, the new methodologies of this research outperforms other models in terms of identifying the order and predicting the future values.

scholarly journals Assessment of Tourism in Nepal with Reference to Regional Countries: Trend Analysis and ARIMA Approach

2021 ◽  
Vol 3 (1) ◽  
pp. 37-53
Author(s):  
Rajendra Man Shrestha ◽  
Aabha Shrestha
Keyword(s):  
Growth Rate ◽  
Trend Analysis ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Information Criterion ◽  
Cultural Property ◽  
Significant Growth ◽  
International Tourist ◽  
Partial Autocorrelation Function ◽  
Per Capita

Tourism (either domestic or international or both) is an internationally flourished business or industry all over the world. The economic foundations of tourism are essentially the cultural assets, the cultural property and the nature of the travel location. So, it has a greater contribution to the country’s balance of payments. Simple trend analysis was carried out using a set of line graphs along simple linear regression. For forecasting of international tourist arrivals of period: 1962-2020, and real per capita international tourist receipts of period: 1995-2018, the suitable Auto-Regressive Integrated Moving Average (ARIMA) models were developed using Akaike Information Criterion along with method of autocorrelation function and partial autocorrelation function. Nepal has a significant growth rate of 1.372 of the international tourist arrivals. It has the eighth position for international tourist arrivals among nine counties. Likewise, Nepal has a significant growth rate of 1.315 of real per capita international tourist receipts. It has the fourth position for real per capita international tourist receipts among nine counties. Nepal has been receiving its international tourist arrivals, growth as well as real per capita international tourist receipts. Forecasts of international tourist arrivals of Nepal are 879638.3 in 2018, 860459.0 in 2019, 875824.1 in 2020, 891189.3 in 2021, and 906554.4 in 2022. Forecasts of real per capita international receipts in dollars are 687000000 in 2019, 727000000 in 2020, 807000000 in 2021, and 845000000 in 2022.

Water Use Trend at Universiti Teknologi Malaysia: Application of ARIMA Model

2012 ◽  
Author(s):  
Ayob Katimon ◽  
Amat Sairin Demun
Keyword(s):  
Time Series ◽  
Water Use ◽  
Autocorrelation Function ◽  
Water Consumption ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
University Campus ◽  
Autoregressive Integrated Moving Average ◽  
Partial Autocorrelation Function

Kertas kerja ini menerangkan aplikasi kaedah permodelan (ARIMA) bagi mewakili perilaku penggunaan air di kampus Universiti Teknologi Malaysia. Menggunakan fungsi–fungsi ACF, PACF dan AIC, siri masa penggunaan air bulanan di kampus UTM boleh dinyatakan dalam model ARIMA (2,0,0). Anggaran parameter model ø1 dan ø2 ialah 0.2747 dan 0.4194. Keadaan tersebut menggambarkan bahawa penggunaan air pada bulan semasa tidak semestinya dipengaruhi dengan tepat oleh kadar penggunaan air pada bulan sebelumnya. Analisis juga menunjukkan model ARIMA (2,0,0) boleh diguna sebagai model ramalan guna air di kampus universiti. Kata kunci: Guna air, kampus universiti, siri masa, model ARIMA The paper describes the application of autoregressive integrated moving average (ARIMA) model to represent water use behaviour at Universiti Teknologi Malaysia (UTM) campus. Using autocorrelation function (ACF), partial autocorrelation function (PACF), and Akaike’s Information Criterion (AIC), monthly campus water use series can be best presented using ARIMA (2,0,0) model. The estimated parameter of the model ø1 and ø2 are 0.2747 and 0.4194 respectively. This implies that water consumption in UTM campus at the present month is not necessarily influenced by water consumption of immediate previous month. Analysis shows that ARIMA (2,0,0) model provides a reasonable forecasting tool for campus water use. Key words: Water use, university campus, time series, ARIMA model

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 Autoregressive Modeling and Forecasts of Degema Monthly Allocation: Buy’s-Ballot and Bartlett’s Transformation

2021 ◽  
pp. 61-75
Author(s):  
Herbert, AfeyaIbibo ◽  
Biu, Oyinebifun Emmanuel ◽  
Enegesele, Dennis ◽  
Wokoma, Dagogo Samuel Allen
Keyword(s):  
Autocorrelation Function ◽  
Akaike Information Criterion ◽  
Selection Criteria ◽  
Linear Trend ◽  
Information Criterion ◽  
Transformation Method ◽  
Autoregressive Modeling ◽  
Data Set ◽  
Trend Pattern ◽  
Partial Autocorrelation Function

The paper focused on Autoregressive modeling and forecasts of Degema Local Government Council Monthly Allocation (DLGCMA) in River State, Nigeria. The Buys-Ballot table and Bartlett’s Transformation method were adopted to identify the trend pattern and to determine the best transformation for the series. The logarithmic transformation was adjudged to be the best and was applied to stabilize the variance. Identification of the trend and stationary for the data set was done and the DLGCMA series showed a linear trend that was non-stationary. The stationarity of the DLGCMA series was obtained after the first difference. The ARIMA models were fitted to the series base on the behaviour of autocorrelation function (ACF) and partial autocorrelation function (PACF). Finally, the model selection criteria called Akaike information criterion was used to determine the best model among the predicted models. The AR(3,1,0) model ( Xt = 0.56Xt-1 + 0.17Xt-2 + 0.64Xt-3 - 0.37Xt-4 + et) was considered to be the best model because it has the least value of the Akaike information criterion (AIC). Hence, the forecasts for the next allocation of twenty-four (24) months ahead were determined.

Predicting Daily Confirmed COVID-19 Cases in India

2021 ◽  
pp. 34-51
Author(s):  
Sudip Singh
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Family Welfare ◽  
Mean Square ◽  
Univariate Time Series ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

scholarly journals Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Rahul Tripathi ◽  
A. K. Nayak ◽  
R. Raja ◽  
Mohammad Shahid ◽  
Anjani Kumar ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Selection Criterion ◽  
Information Criteria ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Rice Productivity ◽  
Rice Area ◽  
Moving Average Models ◽  
Partial Autocorrelation Function

Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA) models and was compared with the forecasted all Indian data. The autoregressive (p) and moving average (q) parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF) and autocorrelation function (ACF) of the different time series. ARIMA (2, 1, 0) model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1) was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC) and Schwarz-Bayesian information criteria (SBC). The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE), which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.

scholarly journals ANALISIS TIME SERIES DENGAN METODE ARIMA DAN APLIKASINYA

2020 ◽  
Vol 1 (2) ◽  
pp. 26-36
Author(s):  
Fathorrozi Ariyanto ◽  
Moh. Badri Tamam
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Partial Autocorrelation Function ◽  
Model Time Series

Model time series yang sangat terkenal adalah model Autoregressive Integrated Moving Average (ARIMA) yang dikembangkan oleh George E. P. Box dan Gwilym M. Jangkins. Model time series ARIMA menggunakan teknik-teknik korelasi. Identifikasi model bisa dilihat dari ACF (Autocorrelation Function) dan PACF (Partial Autocorrelation Function) suatu deret waktu. Tujuan model ARIMA dalam penelitian ini adalah untuk menemukan suatu model yang akurat yang mewakili pola masa lalu dan masa depan dari suatu data time series. Pada penelitian ini, Penulis akan menganalisis penurunan algoritma suatu metode peramalan yang disebut metode peramalan ARIMA Kemudian menerapkan metode tersebut pada data riil yaitu data produksi air di PDAM Pamekasan dengan bantuan komputer dan software SPSS, yang nantinya akan diterapkan di dalam memberikan informasi dan analisis yang akurat terhadap perusahaan PDAM Pamekasan.Dari hasil pembahasan diperoleh rumus ARIMA yang berbentuk: Profit=+Y+Z, kemudian dari hasil penerapan data riil yaitu pada data produksi air di PDAM Pamekasan diperoleh model ARIMA (1 0 0) (0 0 1) sebagai model terbaik. Dengan model : 

scholarly journals Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 240 ◽  
Author(s):  
Mohammed Alsharif ◽  
Mohammad Younes ◽  
Jeong Kim
Keyword(s):  
Time Series ◽  
Solar Radiation ◽  
South Korea ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Sarima Model ◽  
Radiation Data ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

Forecasting solar radiation has recently become the focus of numerous researchers due to the growing interest in green energy. This study aims to develop a seasonal auto-regressive integrated moving average (SARIMA) model to predict the daily and monthly solar radiation in Seoul, South Korea based on the hourly solar radiation data obtained from the Korean Meteorological Administration over 37 years (1981–2017). The goodness of fit of the model was tested against standardized residuals, the autocorrelation function, and the partial autocorrelation function for residuals. Then, model performance was compared with Monte Carlo simulations by using root mean square errors and coefficient of determination (R2) for evaluation. In addition, forecasting was conducted by using the best models with historical data on average monthly and daily solar radiation. The contributions of this study can be summarized as follows: (i) a time series SARIMA model is implemented to forecast the daily and monthly solar radiation of Seoul, South Korea in consideration of the accuracy, suitability, adequacy, and timeliness of the collected data; (ii) the reliability, accuracy, suitability, and performance of the model are investigated relative to those of established tests, standardized residual, autocorrelation function (ACF), and partial autocorrelation function (PACF), and the results are compared with those forecasted by the Monte Carlo method; and (iii) the trend of monthly solar radiation in Seoul for the coming years is analyzed and compared on the basis of the solar radiation data obtained from KMS over 37 years. The results indicate that (1,1,2) the ARIMA model can be used to represent daily solar radiation, while the seasonal ARIMA (4,1,1) of 12 lags for both auto-regressive and moving average parts can be used to represent monthly solar radiation. According to the findings, the expected average monthly solar radiation ranges from 176 to 377 Wh/m2.

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