Exponential Smoothing Technique Research Articles

Remaining useful life prediction of bearings using a trend memory attention-based GRU network

Abstract Remaining useful life (RUL) prediction of bearings holds significant importance in enhancing the reliability and durability of rotating machinery. Bearings undergo a gradual degradation process that unfolds over multiple stages. In this paper, a novel framework for forecasting the RUL of bearings is put forward, which includes the construction of a health indicator with a stage division algorithm (SDA) and the estimation of the health indicator using a new trend memory attention-based gated recurrent unit (TMAGRU). The SDA, based on the K-Means++ algorithm and angle recognition algorithm, is introduced to distinguish the degradation stage based on the health indicator. Inspired by the double exponential smoothing technique and attention mechanism, the proposed TMAGRU network effectively incorporates both the historical health information in the slow degradation stage and its trend. Experimental results conducted on IEEE PHM Challenge 2012 dataset and XJTU-SY dataset demonstrate the superior predictive performance of the proposed approach compared to several state-of-the-art predictive networks.

Modelling Infant Mortality Rate using Time Series Models

The world’s main indicator of children’s health and general development is the infant mortality rate for infant under the age of five. Infant mortality is the term used to describe the death of a child before their first birthday. The infant mortality rate (IMR), which is the number of deaths of infants under one year of age per 1,000 live births, can be used to describe the prevalence of infant mortality in a population. Comparing the death rate of children under the age of five is the child mortality rate, commonly referred to as the under-five mortality rate. Nigeria, one of the nations with a high under-five mortality rate of 117 per 1,000 live births in 2019, is among those nations. The nation is among the top five nations with the highest mortality rate for children under five in 2019. This study aims to model infant mortality(Live birth and Still birth) rate using time series models and to predict the mortality rate using these models. Adeoyo Maternity Hospital Yemetu in Ibadan provided the data for this study. The data set is a monthly data and also a secondary data span for a period of 12 years (2009 to 2020). The time plot showed visual inspection and non-stationarity. Differencing was done and the unit root test performed for the purpose of comparison thereafter. Augmented-Dickey Fuller test and Phillip Perron unit root test was further tested for the establishment of stationarity in order to the main objectives. Three time series methods are the Autoregressive Integrated Moving Average Model(ARIMA), Exponential Smoothing and the Holt-Winters Method were used to model and predict the infant mortality rate data. The result shows that ARIMA order=c(0,0,1) with zero (0) mean for stillbirth and ARIMA order=c(1,0, 2) for live birth with the smallest AIC = (9.102 and 13.991). Akaike Information Criterion(AIC) values of (9.289, 14.139) and (9.102, 13.991) for live birth and still birth, respectively, were derived by exponential smoothing and Holtwinters technique. This means that Holtwinters' technique, which yielded the lowest AIC when compared to ARIMA and exponential smoothing, is the most accurate predictor of both stillbirth and live birth data. Given the high mortality rate for children under the age of five, it is crucial for the government to place more of an emphasis on health issues and to solve the problems plaguing Nigeria's child health care system.

Analysis of air cargo traffic at airports in Nigeria and Ghana

Fluctuations that characterise airport traffic make planning for efficient operations difficult. This study examines air cargo traffic at Murtala International Airport, Lagos, Nigeria, and Kotoka International Airport, Accra, Ghana. The study focused on the trend of cargo volume, origin, and destination and forecasted the cargo volume at the airports. Air cargo data from 1991-2022 were collected from the Federal Airport Authority of Nigeria (FAAN) and Ghana Airport Company Limited (GACL). Descriptive techniques using line graphs and GIS mapping were used to analyse data. Time series techniques of moving and weighted averages, exponential smoothing, and least square techniques were used to forecast the cargo volume of the airports. The study found a characteristic fluctuating pattern of cargo flow. Nigeria’s dominating export cargo types are general goods, edible items, and hair attachments. Ghana’s dominating export cargo types were pineapple, general goods, and papaya. Germany, followed by Ethiopia and Turkey, dominate the origin of import cargo, while the United Kingdom, UAE, and Turkey dominate the destination of export cargo from Nigeria. Luxembourg, Turkey, and the UK dominate the origin of import cargo, while the UK, Netherlands, UAE, and Qatar dominate the destination of export cargo in Ghana. The least-square technique predicts 3.67% and 2.3% growth of cargo traffic in Nigeria and Ghana, respectively. An increasing trend in cargo volume was predicted for both airports. Both countries need to strategise on their relationships with other countries and develop policies that will increase cargo flow by air.

Cacao (Theobroma cacao) Production Forecasting in Lampung Province with the Double Exponential Smoothing Method

Cacao (Theobroma cacao) is one of the Indonesian plantation commodities which can be processed into cacao and chocolate products which contain natural antioxidants. In Lampung, the cacao commodity is very superior because every year it has significantly increased and cocoa production is one of the plantation commodities that has contributed to the export development of Lampung Province. In this case, forecasting can be used to estimate that cacao production will always experience an increase or decrease. Another function is to provide information for farmers and the government. Forecasting uses the Double Exponential Smoothing method because the technique is suitable for forecasting with trend patterns and for the long term. The results and discussion of this research is that cacao production has increased in forecasting for 10 years with good MAPE criteria, namely 10.81484. From the discussion above, forecasting cocoa production using the Exponential Smoothing method, namely the Double Exponential Smoothing technique assisted by R software, is suitable for use because it produces good forecasts and a low forecasting accuracy value.

Non-volatile resistive switching behavior and time series analysis of Ag/PVA-graphene oxide/Ag device

Non-volatile memory devices have been getting significant attention from researchers worldwide in recent years due to their application in resistive random access memory and neuromorphic computing. Here, we have fabricated polyvinyl alcohol-graphene oxide (PVA-GO) composite as an active material for the resistive switching with different concentrations of GO (0.0, 0.1, 0.2, 0.3, 0.4, and 0.5 wt. % GO solution) dispersed in 5 wt. % PVA matrix in a 2:1 volume ratio. We demonstrate the non-volatile forming free resistive switching properties of Ag/PVA-GO/Ag devices. Structural properties of PVA-GO composites are established from the x-ray diffraction pattern, which indicates the complete dispersion of GO inside the PVA matrix. The Ag/PVA-GO-0.1 wt. %/Ag device shows better bipolar resistive switching at VSET ∼ 0.4 V and VRESET at ∼−0.8 V. This device indicates well-resolved two distinct states at a read voltage of 0.1 V in endurance and retention measurements. The fabricated device switches successfully tested for 2.5 × 103 cycles and retains its state for 3.36 × 103 s without any observable degradation. Furthermore, the non-volatile retention property was modeled using time series analysis. For this, Holt–Winter's exponential smoothing technique was utilized. Additionally, the charge–flux linkage characteristic shows the double-valued function, and time domain–charge and time domain–flux show asymmetric behaviors. The electrical conduction mechanism exhibits ohmic behavior in the entire region of the low resistance state and the lower voltage region of the high resistance state. In the high-voltage region of the high resistance state, the space charge-limited conduction mechanism is observed. The resistive switching behavior is explained with the help of an appropriate model.

Multi-Network-Based Ensemble Deep Learning Model to Forecast Ross River Virus Outbreak in Australia

Ross River virus (RRV) disease is one of the most epidemiological mosquito-borne diseases in Australia. Its major consequences on public health require building a precise and accurate model for predicting any forthcoming outbreaks. Several models have been developed by machine learning (ML) researchers, and many studies have been published as a result. Later, deep learning models have been introduced and shown tremendous success in forecasting, mainly the long short-term memory (LSTM), which performs significantly better than the traditional machine learning approaches. There are four common problems that previously developed models need to solve. They are exploding gradient, vanishing gradient, uncertainty and parameter bias. LSTM has already solved the first two problems, i.e. exploding and vanishing gradient problems, and the remaining two are overcome by [Formula: see text]-LSTM. However, developing a prediction model for the RRV disease is a challenging task because it presents a wide range of symptoms, and there needs to be more accurate information available on the disease. To address these challenges, we propose a data-driven ensemble deep learning model using multi-networks of LSTM neural network for RRV disease forecasting in Australia. Data is collected between 1993 and 2020 from the Health Department of the Government of Australia. Data from 1993 to 2016 is taken to train the model, while the data of 2016–2020 is used as a test dataset. Previous research has demonstrated the efficacy of both ARIMA and exponential smoothing techniques in the field of time-series forecasting. As a result, our study sought to evaluate the performance of our proposed model in comparison to these established parametric methods, including ARIMA and ARMA, as well as the more recent deep learning approaches such as encoder–decoder and attention mechanism models. The results show that [Formula: see text]-LSTM achieves higher accuracy and has a less mean-square error. We have also discussed the comparison of the models in detail. Such forecasting gives an insight into being well prepared and handling the situation of the outbreak.

Prediction of macroeconomic variables of Pakistan: Combining classic and artificial network smoothing methods

This research concentrates on using neural networks in the modelling and prediction of macroeconomic variables in specific. Macroeconomic predictors are particularly interested in neural networks because of their capacity to predict any linear or non-linear connection with a decent degree of precision. Two macroeconomic variables have been used for projecting: gross domestic product (volume, NGDPD) and total investment (NID NGDP) over the time period of 2013–2023. Moving averages, exponential smoothing, Brown’s single-parameter linear, exponential smoothing, Brown’s second-order exponential smoothing, Holt’s two-parameter linear exponential smoothing, and decomposition techniques are used as analytical tools. The research focuses on the usefulness of the artificial neural network model for predicting economic determinants in the long run and compares the ANN’s findings with the Conventional Time - Series data sets (Smoothing & Decomposition Techniques). To emphasise the point, a scientific illustration is used to forecast Pakistan’s two crucial macroeconomic indicators. Based on the empirical results, ANN can play a vital role in forecasting the macroeconomic fundamentals of Pakistan if compare to Exponential smoothing techniques.

Safety evaluation of centerline rumble strips on rural two-lane undivided highways: Application of intervention time series analysis

Centerline rumble strips are low-cost effective countermeasures installed on the center of the highway segments to reduce crashes, especially roadway departure crashes. For safety evaluation of centerline rumble strips, methodologies such as naïve before-after analysis and cross-sectional study with Empirical Bayes have been widely utilized. The implementation of these methodologies may be limited due to the lack of relevant control groups, and/or other temporal variations in crashes such as seasonality and serial autocorrelation. This study aims to explore Intervention Time Series Analysis approach as an alternative method for the safety evaluation of centerline rumble strips on rural-two-lane undivided highways in Louisiana. Two different methodologies are explored in the intervention time series approach including the Forecast modeling technique and the Auto-regressive Integrated Moving Average intervention model. The forecast models are based on the exponential smoothing technique, state-space framework, and neural network model. The database consists of monthly observations of total and target crashes on 312 highway segments of 1274 miles in length in which centerline rumble strips were installed during the 2010–2012 period. The time frame 2005–2012 is defined as the pre-intervention period whereas the time frame 2013–2017 is defined as the post-intervention period. The analysis revealed that the Auto-regressive Integrated Moving Average intervention model performed better in terms of error estimates including root means square error, mean absolute error, and mean absolute percentage error. The proposed Auto-regressive Integrated Moving Average intervention model reveals a 17.75% total and 40.54% target crash reduction on the selected rural-two-lane undivided highway segments during the post-intervention period. All the findings are found statistically significant at a 95% confidence level.

Forecasting Stock Prices on the Basis of Technical Analysis in the Industrial Sectors of the UK Stock Market

This study aims to evaluate, critically and rigorously the weak-form market efficiency and forecasting power of technical analysis in different industries in the London Stock Exchange. Weekly data were collected from the FTSE-all share index, FTSE-350 general industrial index and twenty companies of four different industries, for the period between 1997 and 2017. Bai-Perron’s multiple breaks test was applied to diagnose plain data period for the purpose of forecasting. The statistical inference was made from the application of the runs test, variance ratio tests, Ljung-Box’s test and ADF-unit root test that the market is not weak-form efficient and stock prices are predictable. This study extends the current literature by considering the existence of weak-form inefficiency in different industrial sectors. The findings do not support for weak-form efficiency over the periods tested from the application of the ARIMA and GARCH (1, 1) models and double and triple exponential smoothing techniques

Forecasting daily admissions to an emergency department considering single and multiple seasonal patterns

When dealing with several years of daily data, such as the number of daily admissions to a hospital’s emergency department (ED), how complex does it get to forecast into the future? With that in mind, this study has two main goals: to explore the differences between several methodologies, considering both single and multiple-seasonal patterns; and to select the most suitable model for the administration of a Portuguese hospital to use while managing their ED. To that end, we first considered the data as a time series with a single weekly seasonal pattern. We then modelled the data using time series regression, linear regression with autoregressive integrated moving average (ARIMA) errors, seasonal ARIMA and exponential smoothing techniques. Second, the data was set to be a time series with weekly and annual seasonal patterns. Then, using Fourier terms, we applied time series regression, linear regression with ARIMA errors and trigonometric exponential smoothing state space models with Box–Cox transformation, ARMA errors, Trend and Seasonal components (TBATS) for the analysis. After selecting the best-fitting models using the Akaike Information Criteria (AIC) values, we forecasted into the future and compared the results using both training and test datasets’ root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) values. The time series regression model based on seasonal variables and a weekly seasonal pattern gives the best results. However, we decided to use linear regression with ARIMA errors, seasonal variables, and both weekly and annual seasonal patterns. This produces similar results but allows for the annual seasonality to be considered, which is useful when more data is added.

2D Ti3C2Tx MXene-derived self-assembled 3D TiO2nanoflowers for nonvolatile memory and synaptic learning applications

Two-dimensional (2D) semiconducting materials and transition-metal oxides are promising materials for nonvolatile memory and brain-inspired neuromorphic computing applications. However, it remains challenging to obtain high-quality stacked 2D films with low energy consumptions (or drive currents) because of their high interfacial resistance. In this study, we synthesized 2D Ti3C2Tx MXene-derived three-dimensional (3D) TiO2 nanoflowers (NFs) as a feasible resistive switching (RS) material with outstanding electronic properties and synaptic learning capabilities. The electrical and optical characteristics of the synthesized material were determined through density functional theory calculations. Electrical measurements of the Al/Ti3C2Tx-TiO2 NF/Pt memory device indicated the occurrence of forming-free switching phenomena with extremely low switching voltages (0.68–0.53 V), stable ON/OFF ratio (2.3 × 103), and retention greater than 105 s. The Holt–Winters exponential smoothing technique was used for modeling and predicting the switching voltages of the RS device. The mechanism underlying the reliable RS was confirmed by observing the dense conductive filaments through conductive atomic force microscopy. Interestingly, the 2D Ti3C2Tx MXene-derived 3D TiO2 NF-based RS device mimicked the potentiation/depression and spike-time-dependent plasticity of a biological synapse. Finally, a convolutional neural network was implemented based on the observed synaptic weights of Al/Ti3C2Tx-TiO2 NF/Pt for image-edge detection.

Forecast models to predict the demand for endoscopic procedures in a tertiary unit: a prospective validation.

Over the past few decades, there has been an exponential increase in the utilization of endoscopic procedures. Accurately predicting the demand is crucial for effective capacity planning and resource allocation in the endoscopic unit. However, predictive models are not integrated into current endoscopy software. To overcome this limitation, our group used data on the demand in our tertiary unit from 2015 to 2021 (83 months) to develop forecast models using exponential smoothing techniques adjusted for trend and seasonality (derivation phase). These models were recently published at the Revista Española de Enfermedades Digestivas.

Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment

The road transportation sector in Saudi Arabia has been observing a surging growth of demand trends for the last couple of decades. The main objective of this article is to extract insightful information for the country’s policymakers through a comprehensive investigation of the rising energy trends. In the first phase, it employs econometric analysis to provide the causal relationship between the energy demand of the road transportation sector and different socio-economic elements, including the gross domestic product (GDP), number of registered vehicles, total population, the population in the urban agglomeration, and fuel price. Then, it estimates future energy demand for the sector using two machine-learning models, i.e., artificial neural network (ANN) and support vector regression (SVR). The core features of the future demand model include: (i) removal of the linear trend, (ii) input data projection using a double exponential smoothing technique, and (iii) energy demand prediction using the machine learning models. The findings of the study show that the GDP and urban population have a significant causal relationship with energy demand in the road transportation sector in both the short and long run. The greenhouse gas emissions from the road transportation in Saudi Arabia are directly proportional to energy consumption because the demand is solely met by fossil fuels. Therefore, appropriate policy measures should be taken to reduce energy intensity without compromising the country’s development. In addition, the SVR model outperformed the ANN model in predicting the future energy demand of the sector based on the achieved performance indices. For instance, the correlation coefficients of the SVR and the ANN models were 0.8932 and 0.9925, respectively, for the test datasets. The results show that the SVR is better for predicting energy consumption than the ANN. It is expected that the findings of the study will assist the decision-makers of the country in achieving environmental sustainability goals by initiating appropriate policies.

Video-Based Time-Series Feature Extraction Image Encoding Methods for Enhanced Colorimetric Gas Concentration Estimation

Early detection and intervention of gas leaks can reduce industrial risks. Numerous early detection methods have been developed and applied in the industrial field, but they require professional analysis technology, long analysis time, and relatively expensive equipment. Strategies to reduce industrial gas hazards constantly require the development of new, efficient and reliable devices.In the past few decades, various types of gas sensors have been developed based on semiconductors, optical fibers, acoustic waves and other technologies. A significant amount of work has been done in recent years to make sensors smaller, more sensitive, more visually responsive, more manageable, more personal, and more wearable. However, these functions require expensive equipment, complex skills, and large amounts of power. In this regard, colorimetric sensors are of great interest due to their simplicity, ease of manufacture, low cost, visual response, and ease of interpretation without power consumption. However, current colorimetric sensors have some limitations to overcome, such as sensitivity, high cost, early detection and accuracy for use in industrial safety systems. Conventional colorimetric sensor studies use algorithms to compare discoloration with the naked eye, estimate gas concentrations with simple statistical methods, or estimate simple discoloration information using machine learning algorithms to estimate time-series data. Algorithms are trained on measured images or time series data extracted from images. Signal processing focuses on denoising done by scaling time series data. These studies using machine learning and deep learning-based analytics for colorimetric sensor studies did not give sufficient consideration to data coding. Machine learning of time series data is performed using a time series decomposition method that separates time dependencies. I am trying to analyze time-series data through image encoding.In this study, a time series data analysis technique using CNN (Convolutional Neural Network) including multidimensional image encoding was applied to improve the gas concentration estimation accuracy. The estimation accuracy obtained in this study was higher than the estimation accuracy obtained by applying existing statistical methods such as Auto-Regressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ETS) and machine learning techniques such as Multi-layer Perceptron (MLP).We focused on estimating gas concentrations using a time-series-based colorimetric analysis method developed based on colorimetric fabric detection data. Time series data obtained through gas experiments were encoded into images to classify the color value change trend of gas concentration. Image encoding improves the efficiency of specifying gas-exposed sections and estimating gas concentrations within the entire section. Compared to the concentration estimation accuracy of the existing data classification method, MLP was improved by more than 2.5%. In addition, the accuracy was improved by applying a CNN that learned multidimensional processing of sensor data through image encoding. As a result, it was confirmed that the estimation accuracy was improved by 6.1% compared to the basic estimation technique. Although this method has limited data form and application fields, it is effective for subjects such as ours. The proposed technology can be applied to the next-generation smart industrial safety system. Figure 1

Comparative analysis of ARIMA and double exponential smoothing for forecasting rice sales in fair price shop

One of the most challenging issues during the pandemic is managing uncertainties in demand, customer behavior, and market trends. Such instability and unpredictability resulted in numerous cases of excess stock when demand declined or a shortage of commodities when demand for certain goods increased significantly. The research presented in this paper contributes to modelling and forecasting rice sales demand in a Fair Price Shop (FPS) in Kerala, India by employing a time series technique. Our research shows how past demand data can be used to estimate future demand and how these forecasts impact the Public Distribution System (PDS). Our study employs Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (DES) techniques to develop future prediction models that significantly increase the efficiency and accuracy of demand and inventory forecasting. The forecast models generated from past data are verified and validated in the real case application using the Mean Absolute Percentage Error (MAPE) that helps to forecast the demand of inventory required in FPS. The proposed ARIMA and DES outperform the forecasts made by the empirical model, with ARIMA doing better in terms of future forecasts.

Dissipative output feedback tracking control of Markov jump systems under compensation scheme

This paper is concerned with the problem of dissipative output feedback tracking control for Markov jump systems under compensation scheme. The measured output of the system is assumed to be dropped in a probabilistic way before being transmitted to the controller. By virtue of the single exponential smoothing technique, a novel compensation strategy is proposed to help offset the impact of missing measurements. On this basis, an asynchronous output feedback tracking controller is designed to ensure the stochastic stability and strict dissipativity of the tracking error system. Then, sufficient conditions for the existence of the controller are derived via the mode-dependent and fuzzy-basis-dependent Lyapunov function. Finally, two practical examples are provided to illustrate the validity and applicability of the developed approach.

Comparative Study of the Error Trend and Seasonal Exponential Smoothing and ARIMA Model Using COVID-19 Death Rate in Nigeria

Background: COVID-19 has claimed the lives of millions of people in Nigeria and around the world during the last two years. It is a recognized global health crisis of our day, as well as a persistent threat to the earth. The goal of this study was to examine the trend and fit an Error Trend and Seasonal (ETS) exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) model to Nigeria's COVID-19 daily fatalities.Methods: A dataset of daily COVID-19 confirmed fatality cases was used in the investigation. Data was acquired from the Nigerian Centre for Disease Control (NCDC) web database between the 10th of July 2020 and the 2nd of December 2021. The ARIMA model and twelve (12) ETS exponential smoothing techniques were investigated using a dataset of COVID-19 pandemic deaths in Nigeria. The ARIMA and ETS exponential smoothing algorithms were evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan Quinn Information Criterion (HQC), and Average Mean Squared Error (AMSE) selection criteria.Result: The ARIMA (0,1,0) model was the best time series modeling for the coronavirus (COVID-19) epidemic in Nigeria since it had the lowest AIC=2863.51, BIC=2866.90, HQ = 2866.90, and AMSE = 0.55471 values.Conclusion: The ARIMA (0,1,0) model is preferred above the other thirteen (13) competing models based on daily confirmed COVID-19 deaths in Nigeria. This research would assist the Nigerian government in better understanding the pestilence's evolution pattern and providing adequate provisions, prompt mediation, and treatment to prevent additional deaths caused by the virus.

Integrated ANN-Bayes-based travel time prediction modeling for signalized corridors with probe data acquisition paradigm

Travel time is a major information in support of Transportation System Management and Operations (TSMO). Accurate travel time information extracted from probe data sources, which have been evolved in recently years, has led to the considerable research into travel time prediction modeling on freeways. However, little research has been reported on dealing with predicting signalized arterial travel time. It is mainly because of the natures of interrupted traffic flows along a signalized corridor or dynamic urban network, wherein varying signal timing schemes, crossing traffic, and dynamic route choices make arterial travel time estimations much more challenging than freeways. This paper presents an innovative methodological framework that can be used to integrate the exponential smoothing technique, Artificial Neural Network (ANN) techniques and Bayes algorithms for predicting travel time along a signalized corridor. The data source obtained along the US-27 corridor in the State of Ohio, was used to test two types of the developed models, i.e., hourly instantaneous travel time prediction model and realized (or experienced) route travel time prediction model. The study results indicate that the developed models can effectively capture the travel time patterns by combining an exponential smoothing base profile and an ANN-Bayes-trained dynamic profile. In this way, more sensitive changes can be identified to a variation aroused by non-recurring congestion. The testing results with real-world travel time datasets indicates a good performance of the predicted travel time models. The most errors range from −0.608 to 0.485, which are much lower than the threshold standard deviation if 2.24 and close to 6% of mean travel time value of 9.08 min. Overall, the study results suggest that the proposed methods are capable of capturing traffic patterns even when an incident occurs by comparing with conventional methods.

Comparative Study of the Error Trend and Seasonal Exponential Smoothing and ARIMA Model using COVID-19 Death Rate in Nigeria

In the last two years, COVID-19 had claimed millions of life in Nigeria and the world at large. It is an established global health emergency of our time and an ongoing threat faced by the world up till now. This study aims to determine the trend, fit an appropriate Error Trend and Seasonal (ETS) exponential smoothing and ARIMA model to the COVID-19 daily deaths in Nigeria. Dataset on the daily COVID-19 confirmed death cases were utilized in the study. The data was extracted from the Nigerian Centre for Disease Control (NCDC) online database from 10th July 2020 to 2nd December 2021. Autoregressive Integrated Moving Average (ARIMA) and twelve (12) (ETS) exponential smoothing techniques were compared based on the dataset. The performance of the ARIMA and ETS exponential smoothing methods was investigated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan Quinn Information Criterion (HQC), and AMSE selection criteria. The best time series modeling for the coronavirus (COVID-19) epidemic in Nigeria was the ARIMA (0,1,0) because its model selection criteria showed that it had the lowest value of; AIC=2863.51, BIC= 2866.90, HQ = 2866.90, and AMSE = 0.55471. ARIMA (0,1,0) model is preferred among the thirteen (13) competing models based on daily confirmed deaths due to COVID-19 in Nigeria.

Prediction of palm oil production in Riau Province using the single exponential smoothing method

Riau Province is one of the provinces in Sumatra with the most oil palm plantations. Riau Province was once recognized as Indonesia’s greatest oil-producing province, but with the fall in output owing to decreasing oil reserves, Riau has become a greater palm oil producer. Not unexpectedly, palm oil has surpassed petroleum in terms of foreign exchange in Indonesia since 2010. There are numerous stages in this research, namely problem analysis, data collection, data preprocessing, single exponential smoothing prediction, data modeling, and calculation precision. Based on the findings of the testing, the Single Exponential Smoothing technique with alpha 0.9 may be used to forecast the requirement for prior time series data in the future, particularly in the case of oil palm output in Riau Province. Oil palm production is expected to grow somewhat over the next five years, according to the forecast.