scholarly journals The Inflation Forecasting of Major Cities In East Kalimantan: A Comparison Of Holt-Winters And SARIMA Model

2021 ◽  
Vol 1 (2) ◽  
pp. 1-11
Author(s):  
Regi Muzio Ponziani
Keyword(s):  
Test Data ◽  
Mean Squared Error ◽  
Moving Average ◽  
Training Data ◽  
Sarima Model ◽  
East Kalimantan ◽  
Inflation Forecasting ◽  
Autoregressive Integrate Moving Average ◽  
Economic Nature ◽  
Sarima Models

This research aims to compare the performance of Holt Winters and Seasonal Autoregressive Integrate Moving Average (SARIMA) models in predicting inflation in Balikpapan and Samarinda, two biggest cities in East Kalimantan province. The importance of East Kalimantan province cannot be overstated since it has been declared as the venue for the capital of Indonesia. Hence, inflation prediction of the two cities will give valuable insights about the economic nature of the province for the country’s new capital. The data used in this study extended from January 2015 to September 2021. The data were divided into training and test data. The training data were used to model the time series equation using Holt winters and SARIMA models. Later, the models derived from training data were employed to produce forecasts. The forecasts were compared to the actual inflation data to determine the appropriate model for forecasting. Test data were from January 2015 to December 2020 and test data extended from January 2021 to September 2021. The result showed that Holt-Winters performed better than SARIMA in prediction inflation. The Root Mean Squared Error (RMSE) values are lower for Holt-Winters Exponential Smoothing for both cities. It also predicts better timing of cyclicality than SARIMA model.

scholarly journals Comparison between the Holt-Winters and SARIMA Models in the Prediction of NDVI in an Arid Region in Kenya using Pixel-wise NDVI Time Series

2021 ◽  
Vol 2 (23) ◽  
pp. 1-15
Author(s):  
Mwana Said Omar ◽  
◽  
Hajime Kawamukai
Keyword(s):  
Time Series ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Moving Average ◽  
Additive Model ◽  
Grid Search ◽  
Sarima Model ◽  
Ndvi Time Series ◽  
Sarima Models

Desertification is major issue in arid and semi-arid lands (ASAL) with devastating environmental and socio-economic impacts. Time series analysis was applied on 19 years’ pixel-wise monthly mean Normalized Difference Vegetation Index (NDVI) data. The aim of this study was to identify a time series model that can be used to predict NDVI at the pixel level in an arid region in Kenya. The Holt-Winters and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were developed and statistical analysis was carried out using both models on the study area. We performed a grid search to optimise and determine the best hyper parameters for the models. Results from the grid search identified the Holt-Winters model as an additive model and a SARIMA model with a trend autoregressive (AR) order of 1, a trend moving average (MA) order of 1 and a seasonal MA order of 2, with both models having a seasonal period of 12 months. It was concluded that the Holt-Winters model showed the best performance for 600 ✕ 600 pixels (MAE = 0.0744, RMSE = 0.096) compared to the SARIMA model.

scholarly journals Forecasting Inflation in Latin American Countries Using a SARIMA-LSTM Combination

2021 ◽  
Author(s):  
Rodrigo Peirano ◽  
Werner Kristjanpoller ◽  
Marcel Minutolo
Keyword(s):  
Latin American ◽  
Emerging Economies ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Moving Average ◽  
Inflation Forecasts ◽  
Inflation Forecasting ◽  
Forecasting Inflation ◽  
New Research ◽  
Model Confidence Set

Abstract Inflation forecasting has been and continues to be an important issue for the world's economies. Governments, through their central banks, watch closely inflation indicators to make national decisions and policies. Controlling growth and contraction requires governments to keep a close eye on the rate of inflation. When planning strategic national investments, governments attempt to forecast inflation over longer periods of time. Getting the inflation forecast wrong, can result in significant economic hardships. However, even given its significance, there is limited new research that applies updated methodologies to forecast it, and even fewer studies in emerging economies where inflation may be drastically higher. This study proposes to forecast the inflation rate in emerging economies based on the commonly used Seasonal Autoregressive Integrated Moving Average (SARIMA) approach combined with Long Short Term Memory (LSTM). The results indicate that the proposed model based on the combination of SARIMA and LSTM, have a higher accuracy in inflation forecasts as measured by the Mean Square Error (MSE) of the proposed models over the SARIMA model and LSTM alone. The loss function used is Mean Squared Error (MSE), and the Model Confidence Set (MCS) is used to test the superiority of the models in the economies of Mexico, Colombia and Peru.

Comparing the Forecasting Performance of Seasonal Arima and Holt -Winters Methods of Births at a Tertiary Healthcare Facility in Ghana

2019 ◽  
Author(s):  
George Aryee ◽  
Raymond Essuman ◽  
Robert Djagbletey ◽  
Ebenezer Owusu Darkwa
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Healthcare Facility ◽  
Series Data ◽  
Sarima Model ◽  
Forecasting Methods ◽  
Out Of Sample ◽  
Tertiary Healthcare ◽  
Number Of Births ◽  
Sarima Models

Introduction: Studies have shown periodic variations in the number of births using different mathematical models. A study conducted at the Korle-Bu teaching hospital obtained Seasonal Autoregressive Integrated Moving Average (SARIMA) model on a monthly number of birth for an 11-year data. However, this study did not compare the obtained model with other forecasting methods to determine the method that will best explain the data. This study sought to compare seasonal SARIMA model with Holt-Winters seasonal forecasting methods for an 11-year time series data on the number of births.. Methods: Data were analysed in R software (version 3.3.3). Holt-Winters and seasonal ARIMA forecasting methods were applied to the birth data. The errors of the out – of-sample forecast of these methods were compared and the one with the least error was considered the best forecasting method. Results The in-sample forecasting errors showed that SARIMA (2,1,1) x (1,01,) was the best among the other models. The out-of-sample errors also showed that all the SARIMA models had lower errors compared to the Holt-Winters form of additive and multiplicative methods based on the forecasting accuracy indices of the monthly number of births for an 11-year period. It was also found that the months with very high statistically significant number of births over the period was from March to August. Conclusion: The SARIMA models were superior to the Holt-Winters models. This is essential for optimal forecasting of the number of births for planning and effective delivery of Obstetrics services.

scholarly journals Forecast and error analysis of vegetable production in Haryana by various modeling techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 907-912
Author(s):  
Manoj Kumar ◽  
P. K. Muhammed Jaslam ◽  
Sunil Kumar ◽  
Ashok Dhillon
Keyword(s):  
Random Walk ◽  
Error Analysis ◽  
Test Data ◽  
Moving Average ◽  
Training Data ◽  
Annual Data ◽  
Vegetable Production ◽  
Data Set ◽  
Formidable Challenge ◽  
The Government

Crop forecasting is a formidable challenge for every nation. The Government of India has developed a number of forecasting systems. The national and state governments need such pre-harvest forecasts for various policy decisions on storage, distribution, pricing, marketing, import-export and many more. In this paper, univariate forecasting models such as random walk, random walk with drift, moving average, simple exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) models are considered and analyzed for their efficiency for forecasting vegetable production in the Haryana state. The State annual data on vegetable production were divided into the training data set from 1966-67 to 2013-14 and the test data set from 2014-15 to 2018-19. Suitable models were selected on the basis of error analysis on the training data and a percent error deviation test on the test data. Model diagnostic checking was carried out on ACF and PACF in residual terms through runs above and below the median, runs up and down and Ljung-Box tests. It is inferred that ARIMA (2,1,1) was found to be optimal and that the forecast values for the years 2019-20 to 2023-24 were estimated on the basis of this model, which were 7.82,8.23,8.72,9.2 and 9.72 million tonnes for the year 2019-20 to 2023-24, respectively.  The significance of the mode is that we can forecast the values using this best fit model and forecast values are very important for the policymakers and other government agencies for proper policy decision regarding food security.

scholarly journals Comparison of Short-Term Load Forecasting Based on Kalimantan Data

2021 ◽  
Vol 5 (2) ◽  
pp. 243-259
Author(s):  
Syalam Ali Wira Dinata ◽  
Muhammad Azka ◽  
Primadina Hasanah ◽  
Suhartono Suhartono ◽  
Moh Danil Hendry Gamal
Keyword(s):  
Time Series ◽  
Model Building ◽  
Least Squares Method ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Short Term ◽  
Sarima Model ◽  
East Kalimantan ◽  
Moving Average Model ◽  
Short Term Forecasting

This paper investigates a case study on short term forecasting for East  Kalimantan, with emphasis on special days, such as public holidays. A time series of load demand electricity  recorded at hourly intervals contains more than one seasonal pattern.  There is a great attraction in using a modelling time series method that is able to capture triple seasonalities.  The Triple SARIMA model has been adapted for this purpose and competitive for modelling load.  Using the least squares method to estimate the coefficients in a triple SARIMA model, followed by model building, model assumptions  and comparing model criteria, we propose and demonstration  the triple Seasonal Autoregressive Integrated Moving Average model  with AIC 290631.9 and SBC 290674.2 as the best model for this study. The Triple seasonal ARIMA is one of the alternative strategy to propose accurate forecasts of  electricity load Kalimantan data for planning, operation  maintenance and  market related activities.

scholarly journals Inflation Forecasting for East Kalimantan Province Using Hybrid Singular Spectrum Analysis- Autoregressive Integrated Moving Average Model

2021 ◽  
Vol 18 (1) ◽  
pp. 78-92
Author(s):  
Melisa Arumsari ◽  
Sri Wahyuningsih ◽  
Meiliyani Siringoringo
Keyword(s):  
Hybrid Model ◽  
Spectrum Analysis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Percentage Error ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Singular Spectrum ◽  
East Kalimantan ◽  
Inflation Forecasting

The Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA) hybrid method is a good combination of forecasting methods to improve forecasting accuracy and is suitable for economic data that tends to have trend and seasonal patterns, one of which is inflation data. The purpose of this study is to obtain the results of inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA hybrid model. The results of the inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA(1,1,1) hybrid model overall experienced an increase and the highest inflation in 2021 occurred in December of 0.92% with a forecasting accuracy level based on the Root Mean Square Error (RMSE) was 0.069399 and Mean Absolute Percentage Error (MAPE) was 32.61084%  

scholarly journals A New Empirical Correlation for Estimation of EBF Steel Frame Behavior Factor under Near-Fault Earthquakes Using the Genetic Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Seyed Abdonnabi Razavi ◽  
Navid Siahpolo ◽  
Mehdi Mahdavi Adeli
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Mean Squared Error ◽  
Time History ◽  
Training Data ◽  
Geometrical Properties ◽  
Behavior Factor ◽  
Near Fault ◽  
Different Types ◽  
Near Fault Earthquakes

The most important feature of the behavior factor is that it allows the structural designer to be able to evaluate the structural seismic demand, using an elastic analysis, based on force-based principles quickly. In most seismic codes, this coefficient is merely dependent on the type of lateral resistance system and is introduced with a fixed number. However, there is a relationship between the behavior factor, ductility (performance level), structural geometric properties, and type of earthquake (near and far). In this paper, a new and accurate correlation is attempted to predict the behavior factor (q) of EBF steel frames, under near-fault earthquakes, using the genetic algorithm (GA). For this purpose, a databank consisting of 12960 data is created. To establish different geometrical properties of models, 3−, 6−, 9−, 12−, 15, and 20− story steel EBF frames were considered with 3 different types of link beam, 3 different types of column stiffness, and 3 different types of brace slenderness. Using nonlinear time history under 20 near-fault earthquake, all models were analyzed to reach 4 different performance levels. 6769 data were used as GA training data. Moreover, to validate the correlation, 2257 data were used as test data for calculating mean squared error (MSE) and correlation coefficient (R) between the predicted values of (q) and the real values. In addition, the MSE and R were calculated for correlation in the train and test data. Also, the comparison of the response of maximum inelastic displacement of 5 stories EBF from the proposed correlation and the mean inelastic time-history analysis confirms the accuracy of the estimate relationship.

scholarly journals Forecasting the COVID-19 Increment Rate in DKI Jakarta Using Non-Robust STL Decomposition and SARIMA Model

2021 ◽  
Vol 7 (1) ◽  
pp. 21-30
Author(s):  
Rosmelina Deliani Satrisna ◽  
Aniq A. Rohmawati ◽  
Siti Sa’adah
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Sarima Model ◽  
Forecasting Accuracy ◽  
Modeling And Analysis ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
Daily Number ◽  
Increment Rate ◽  
Sarima Models

The Corona virus known as COVID-19 was first present in Wuhan, China at this time has troubled many countries and its spread is very fast and wide. Data on daily confirmed COVID-19 cases were collected from the DKI Jakarta province between early May 2020 and late January 2021. The daily increase in confirmed COVID-19 cases has a percentage of the value of increase in total cases. In this study, modeling and analysis of forecasting the increment rate in daily number of new cases COVID-19 DKI Jakarta was carried out using the Seasonal-Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. STL Decomposition is a form of algorithm developed to help decompose a Time Series, and techniques considering seasonal and non-stationary observation. The results of the best forecasting accuracy are proven by STL-ARIMA, there are MAPE and MSE which only have an error value of 0.15. This proposed approach can be used for consideration for the DKI Jakarta government in making policies for handling COVID-19, as well as for the public to adhere to health protocols.

scholarly journals How to improve infectious disease prediction by integrating environmental data: an application of a novel ensemble analysis strategy to predict HFMD

2021 ◽  
Vol 149 ◽  
Author(s):  
Junwen Tao ◽  
Yue Ma ◽  
Xuefei Zhuang ◽  
Qiang Lv ◽  
Yaqiong Liu ◽  
...  
Keyword(s):  
Variable Selection ◽  
Mean Squared Error ◽  
Moving Average ◽  
Dynamic Bayesian Networks ◽  
Environmental Data ◽  
Control Measures ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Analysis Strategy ◽  
Ensemble Analysis

Abstract This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (−24.88%; t = −5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (−16.69%; t = −4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.

scholarly journals Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia

2021 ◽  
Vol 13 (1) ◽  
pp. 148-160
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Ahmad Mohiddin Mohd Ngesom
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Human Movement ◽  
Population Movement ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Predominant Species ◽  
Lower Confidence ◽  
Dengue Transmission ◽  
Stage 1

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA. By comparing the magnitude of the gradient of decrease, the trend is 319% steeper than the trend observed in previous years and 650% steeper than the simulated model, indicating that the control of population movement did reduce dengue transmission. However, starting from stage 2 of lockdown, the dengue incidences demonstrated an elevation and earlier rebound by four weeks and grew with an exponential pattern. We revealed that Aedes albopictus is the predominant species and demonstrated a strong correlation with the locally reported dengue incidences, and therefore we proposed the possible diffusive effect of the vector that led to a higher acceleration of incidence rate.

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