ARIMA Method Research Articles

Forecasting Drinking Water Sales Values with Artificial Neural Networks: A Comparative Analysis with ARIMA and Winters Methods

Forecasting Drinking Water Sales Values with Artificial Neural Networks: A Comparative Analysis with ARIMA and Winters Methods

Determinants JCI of the Indonesian Stock Exchange (Comparison of IHSG Projection of OLS Method and ARIMA and GARCH Methods for the Period 2015-2023)

This study aims to examine the effect of interest rate, exchange rate, inflation, gross domestic product and LQ-45 index variables on the movement of the Composite Stock Price Index using the ordinary least square method. Comparison in predicting the movement of the stock price index through three models, and then compared with the projection results with OLS, ARIMA and GARCH methods. This research was conducted using monthly data on JCI, interest rates, exchange rates, inflation, gross domestic product and the LQ-45 index for the period May 2015 - December 2023. From the results it can be concluded that interest rates have a significant negative effect on JCI, exchange rates have a significant negative effect on JCI, inflation has a significant negative effect on the value of JCI, gross domestic product has a significant negative effect on JCI and the LQ-45 index has a positive effect on JCI. The results of the comparison of JCI projections with 3 models showed that the OLS model offers more accurate results and has the lowest MAPE value in predicting stock price movements.

Analysis of Swallow Nest Nitrite Levels and Export Volume Projections: A Statistical Approach to Quality Improvement and Global Market Development

The global SARS-CoV-2 pandemic is impacting public health and living systems. Edible Bird's Nest (EBN), one of the variants of Swallow's Nest (SBW), is recognized as a medicinal food. The potential of SBW as a therapeutic agent is gaining attention due to its ability to inhibit viral hemagglutination activity. This study provides information on the prediction of SBW demand in the coming year so that it can contribute to determining Indonesia's SBW export performance. Net weight data (tons) of SBW export time series were obtained for 10 years from 2012 to 2022 in 10 countries, namely: Hong Kong, China, Singapore, USA, Vietnam, Canada, Taiwan, Thailand, Japan, and Cambodia, combined with laboratory examination on nitrite levels conducted at the Surabaya Agricultural Quarantine Center, Indonesia. This study shows the minimum limit on nitrite levels in SBW exports and the need for SBW exports in the next four years. One of the requirements for SBW exports is the minimum limit of nitrite levels. The ARIMA method has been implemented to forecast SBW export demand in 10 countries for the next four years. SBW export demand in the next four years is expected to decline. The findings make a significant contribution as a source of information for decision-makers involved in SBW export activities.

Analysis of New Energy Vehicle Development in China Based on LSTM and ARIMA Models

This study comprehensively analyses the development factors of new energy vehicles in China based on data and literature, adopts principal component analysis to reduce the dimensionality to deal with the influence indicators, and combines multiple regression and LSTM model to predict the development trend of the industry. For the relationship between electrification and the ecological environment, multiple linear regression and ARIMA methods are tried to explore the impact of carbon emissions on the ecological environment. These methods provide a comprehensive perspective for a comprehensive understanding of the development of new energy vehicles and provide a reference basis for future industrial development and ecological environment improvement.

Forecasting Electric Vehicle Sales with ARIMA and Exponential Smoothing Method: The Case of India

Forecasting Electric Vehicle Sales with ARIMA and Exponential Smoothing Method: The Case of India

An improved convolutional recurrent neural network for stock price forecasting

<p>Stock price forecasting is a challenging area of research, particularly due to the complexity and unpredictability of financial markets. The accuracy of prediction models is influenced by various factors, including nonlinearity, seasonality, and economic shocks. Deep learning has demonstrated better forecasts of stock prices than traditional approaches. This study, therefore, proposed a new approach to improve forecasting system based on an end-to-end convolutional recurrent neural network (CRNN) with attention mechanism. Our approach first investigates local stock price features using 1D convolutional neural network, and then employs a bidirectional long short-term memory (Bi-LSTM) network for forecasting. This model stands out by effectively utilizing contextual data and representing the temporal character of data. The Bi-LSTM is helpful for understanding the history and future contextual information since it uncovers both past and future contexts of stock data. Furthermore, integrating attention mechanism within the CRNN represents a significant improvement. This allows our model to handle long input sequences more effectively and capture the inherent stochasticity in stock prices, which is often missed by traditional models. The effectiveness of our approach is investigated using data on 10 stock indexes from Yahoo Finance. The results show that our method outperforms ARIMA, LSTM, and conventional methods.</p><p> </p>

Functional Time Series Models for K-Step Prediction of Sugar Production: A Case Study of Eswatini, Kenya, and South Africa

Sugar, produced from cane or beet, is a vital energy source and a globally traded commodity. Most business happens in a futures exchange market where speculators, hedgers, and institutional consumers and producers, make decisions based on their understanding of the future supply and demand situation. Sugar is sometimes bought and sold before the cane or beet is planted. Hence, improving sugar production forecast accuracy is vital to maximize gains and enable effective planning. The study considered the annual sugar production total as a function of the monthly output rather than a scalar quantity and analyzed historical data using three functional time series techniques. In particular, the methods used for k-steps and dynamic updating prediction of sugar production include Local Polynomial Regression (LPR), Ridge Regression (RR), and Penalized Least Squares (PLS). The performance of the three models was compared to that of the automatic ARIMA method using the Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) measures to establish the suitability of each technique in sugar production forecasting. The LPR, RR, and PLS outperformed the ARIMA method in all three cases. However, the prediction accuracy was lower for highly volatile datasets from countries that overly depend on rainfall for cane development.

Detecting anomalous WM/reuters fixes using Trailing Contextual Anomaly Detection

We propose a Trailing Contextual Anomaly Detection (TCAD) model to detect abnormal movements in the WM/Reuters foreign exchange benchmark setting. By leveraging the high correlation levels among currencey pairs, we demonstrate that the TCAD model outperforms ARIMA, Jump Test, and CAD methods in detecting idiosyncratic cross-sectional anomalies. Additionally, we find that adjusting for intraday seasonality enhances the models' ability to predict on market close manipulation. Furthermore, we quantify and identify abnormal fix movements as high-impact events.

Chili Price Prediction One Year Ahead Using the Gated Recurrent Unit Method

Chili is an important commodity in the Indonesian economy, and it experiences significant price fluctuations. This is due to several factors, such as demand and supply in the market and climate and temperature factors. The prediction of chili prices is important to minimize the risk of loss to the community and the government. Previous studies have used ARIMA, SVR, MLP, and RNN methods to predict chili prices. However, these methods are still considered less effective, especially in processing large amounts of information. In this study, the Gated Recurrent Unit (GRU) method is used as an alternative, and this method is designed to overcome obstacles to remembering long information. The results show that GRU provides excellent prediction performance, with a MAPE value below 10% for all types of chili peppers. The test results that have been carried out show that the size of the window data affects the accuracy of predicting chili prices. In addition, the combination of hyperparameters, namely hidden neurons and epochs, also influences the accuracy of predicting chili prices. The lowest MAPE value is obtained in predicting large red chili pepper prices of 4.850% with 512 hidden neurons and 100 epochs. Then, a MAPE of 6.434% was obtained using 512 hidden neurons and 100 epochs to predict curly red chili pepper prices. In predicting the price of green bird's eye pepper, a MAPE of 7.288% is obtained with a combination of 512 hidden neurons and 150 epochs. Finally, the lowest MAPE value is obtained in predicting the price of red bird's eye pepper at 6.452% with a combination of 512 hidden neurons and 150 epochs.

Perbandingan Metode ARIMA dan SARIMA Dalam Peramalan Jumlah Penumpang Bandara Provinsi Kepulauan Bangka Belitung

One of the transportation that is often used by people in Bangka Belitung to travel outside the area is airplanes. Judging from the data on the number of passengers at two airports in the Bangka Belitung Islands Province, the highest number of airplane passengers departing outside the region in April 2023 was 88,511 people. If there isan excessive increase in the number of passengers, it will have an impact on the quality of airport service levels and disrupt the stability of flight traffic. The purpose of this study is to compare the results of forecasting the number of passengers at the airport of Bangka Belitung Islands Province for the future period using the ARIMA and SARIMA methods. Based on the results of the analysis, the best method to forecast the number of passengers at the Bangka Belitung Islands Provincial Airport is the ARIMA method with the best model, namely ARIMA (0,1,1). In general, the results of forecasting the number of passengers at the Bangka Belitung Islands Provincial Airport from May 2024 to April 2026 will increase the number of passengers continuously until April 2026.

Assessing Extreme Weather Risks for Insurance Underwriting with ARIMA and Entropy Weight Method: Insights from Brazil and India

Assessing Extreme Weather Risks for Insurance Underwriting with ARIMA and Entropy Weight Method: Insights from Brazil and India

LQ45 Stock Price Forecasting: A Comparison Study of Arima(p,d,q) and Holt-Winter Method

The Holt-Winter method and ARIMA(p,d,q) are two frequently used forecasting techniques. When using ARIMA, errors are expected to be connected with earlier errors because it is based on data correlation with prior data (autoregressive) (moving average). The Holt-Winter model comes in two different forms: Multiplicative Holt-Winter and Additive Holt-Winter. No one has ever attempted to compare combined time series and cross-section data, despite the fact that there has been a great deal of prior study on ARIMA and Holt-Winter. In a combined time-series and cross-section dataset, the accuracy rates of Holt-Winter and ARIMA(p,d,q) will be compared in this study. LQ45 stock prices are used because they track the performance of 45 stocks with substantial liquidity, sizable market caps, and solid underlying businesses. The Mean Absolute Percentage Error (MAPE) method is used to gauge accuracy. This study contributes to MAPE exploration by using a Boxplot diagram from cross-sectional data. With the Boxplot diagram, we can see the MAPE spread, the MAPE's center point, and the presence of outliers from the MAPE of LQ45 stock. According to the findings of this empirical study, the average error rate for predicting LQ45 stock prices using ARIMA is 7,0390%, with a standard deviation of 7,7441%; for multiplying Holt-Winter, it is 29,3919%, with a standard deviation of 25,7571%; and for additive Holt-Winter, it is 18,0463%, with a standard deviation of 18,3504%. Apart from numerical comparisons, it can also be seen visually, based on the Boxplot diagram, that the MAPE of ARIMA(p,d,q) is more focused than Holt-Winter. In addition, in terms of accuracy distribution, it can be seen that the MAPE accuracy of the ARIMA method produces four outliers. Based on the MAPE accuracy rate, we conclude that Holt-Winter has a bigger error based on the MAPE value than ARIMA(p,d,q) at forecasting LQ45 stock prices.

Analisis Pengaruh Forecasting Demand Terhadap Efisiensi Manajemen Persediaan

This study analyzes the impact of demand forecasting on inventory management efficiency, focusing on the implementation of the ARIMA method in the context of a manufacturing company in Indonesia. Using a quantitative case study approach, the research explores the relationship between forecasting accuracy and key inventory performance metrics. Results indicate that improved forecasting accuracy significantly enhances inventory efficiency, with the ARIMA method outperforming traditional methods. The implementation of ARIMA resulted in a 36.8% increase in Inventory Turnover Ratio and a 53.3% decrease in stockout rate. Time series analysis revealed a long-term improvement trend in inventory efficiency with significant seasonal components. This research provides valuable insights for practitioners and academics on the critical role of demand forecasting in optimizing inventory management and the potential of the ARIMA method in improving companies' operational efficiency.

APPLICATION OF THE ARIMA METHOD IN FORECASTING THE PRICE RED CAYENNE PEPPER IN MAKASSAR CITY

Red chili is one of the commodities with very tall cost changes. The cost variance of red chili can be caused by a huge amount of supply and request. The higher the amount of supply, the lower the cost, and the lower the amount of supply, the higher the cost. This study aims to implement the ARIMA method in forecasting red cayenne pepper prices in Makassar City. Data analysis to forecast red cayenne pepper prices used the ARIMA method with the results show that the price range of chili is from IDR 13,000 to IDR 80,000, with a mean value of IDR 38,218. The model with the minimum SSE and MSE value is ARIMA(1,1,1), so this model be used in time series data modeling for forecasting. The results of forecasting using the best model obtained a MAPE value of 15.90%, which is in the range of 10-20%, so it can be concluded that the ability of the ARIMA(1,1,1) model in forecasting the price of red cayenne pepper includes the good category.

Prediksi Harga Material Bangunan Dengan Autoregressive Integrated Moving Average (Arima) Pada CV. TJA

The problem most often faced by CV TJA is that the price from the RAB (Cost Budget Plan) calculation is not in accordance with market prices, so it is necessary to predict the price of building materials to help the company prepare the RAB. The aim of this research is to examine predictions of material prices measured using the ARIMA method. The data analysis method in this study used the MA parameter (1) with the ARIMA model (0,1,1) for dynamix cement material, the MA parameter (1) ARIMA model (0,2,1) for cast sand, the MA parameter (1 ) ARIMA model (0,3,1) for threaded iron 13 and AR parameter (2) ARIMA model (2,1,0) for usuk wood material. By using 3 error testing methods, namely Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD) and Mean Squared Deviation (MSD). The results of this analysis show the lowest accuracy value, namely for dynamic cement material with a MAPE value of 5%, a MAD value of 1.986 and an MSD value is 63.584.667. The results of error tests using MAPE, MAD and MSD can be concluded that the ARIMA method is very accurate because the MAPE value is less than 10%.

Clustering and Trend Analysis of Priority Commodities in the Archipelago Capital Region (IKN) using a Data Mining Approach

The policy of moving the capital from Jakarta to East Kalimantan planned by the President of the Republic of Indonesia Joko Widodo has caused a lot of polemic among the public. There are quite a few positive and negative comments on social media regarding the policy of moving the capital. The process of moving the capital requires careful preparation. One thing that needs to be considered is food security in IKN. This research provides recommendations for the main food commodities in IKN by applying data mining. We collect food productivity data available on the official website for East Kalimantan province. These data are processed and grouped into two groups, namely horticulture and livestock products using the K-Means method. After grouping, we predict the increase in productivity of each group using the ARIMA method. This research produces output in the form of grouping commodities into horticulture and livestock products. Productivity results for each type of commodity are displayed from 2016 to 2020 based on data on the official East Kalimantan Province website. Based on this data, predictions are made using the ARIMA method to predict productivity results from 2021 to 2025. Commodities with total productivity are grouped into high-priority commodities. Grouping the amount of productivity is carried out using the clustering method by comparing the amount of productivity for each commodity and producing commodities that are low priority, middle priority, priority and top priority based on the highest to lowest productivity numbers. The cluster quality for grouping horticultural commodities is 99.1%, while the cluster quality for grouping livestock commodities is 87.5%. Hasil prediksi terbaik yaitu ketika memprediksi produksi salak dan slaughter cattle dengan model ARIMA (0, 1, 0) dan ARIMA (2, 2, 2).

Selection of Modelling for Forecasting Crude Palm Oil Prices Using Deep Learning (GRU & LSTM)

The unstable crude palm oil (CPO) prices have an impact on assessments of economic growth and environmental sustainability, as well as market strategies, international trade discussions, and consumer pricing expectations for products made from CPO. Therefore, it is crucial to identify the best prediction method to accurately forecast this cost. This research aims to develop an accurate time series data prediction model for crude palm oil prices using GRU and LSTM methods. The study also aims to identify the best-performing model by comparing their performance. This study uses LSTM and GRU methods as well as Bi-LSTM as a comparison, using crude palm oil price data from the Indonesian Ministry of Trade (August 1, 2018–August 31, 2023) from Medan (1064 data points) and Rotterdam SPOT (617 data), Rotterdam Forward 1 Month (1,022 data), and Rotterdam Forward 2 Month (950 data). Each dataset is then split into training and testing data with a 70:30 ratio. The hyperparameters used are (a) learning rate: (0.001); (b) batch size: 100; (c) node: (512); (d) optimizer: Adam; (e) and epoch: 50. The results of the forecast are highly accurate with a MAPE of less than 10%. Overall, both LSTM and GRU techniques demonstrate excellent performance in forecasting crude palm oil prices, but they may need to be modified based on data features. The ARIMA method can also be considered for forecasting. Future studies may consider optimizing parameters and model structures. Doi: 10.28991/ESJ-2024-08-03-05 Full Text: PDF

APPLICATION OF CAUSAL FORECASTING METHOD TO FORECAST SHALLOT PRODUCTION IN NORTH SUMATRA PROVINCE

Shallot are one of the superior horticultural commodities that has a great influence on the economic value and daily needs of society. The province of North Sumatra is a strategic region for producing shallot, making the province the eighth largest producer of shallot in Indonesia. The need and consumer demand for shallot along with the increasing population is a problem, as by 2021 the amount of shallot production in the household sector will only be met by 11% in the North Sumatra province. The authors are therefore interested in studying the factors that influence shallot production and predicting the amount of shallot production in the future North Sumatra province. The source of data the research was carried out by the Agricultural Department of the Province of North Sumatra. The study was conducted using Causal Forecasting and ARIMA methods. The causal forecasting method used is the econometric method. The econometric method is a method for analyzing and predicting future conditions by finding and measuring several important independent variables and their influence on the variables. Dependents are observed. The ARIMA method is used to predict exogenous variables from the results of the analysis performed. Based on the analysis, it is obtained that the factors affecting the amount of shallot production are the quantity of productivity and the extent of the onion harvest. The greater the amount of productivity and the size of the harvest, the more shallot production will increase. The result of the production forecast obtained is the lowest amount of shallot production occurred in April 2022 at 5212,763 tons and the highest amount produced on the onion occurs in March 2023 at 6502,112 tonnes and the average monthly amount of production is 5856,886 tons.

Particulate matter concentrations around natural gas-fired power plants and their associated health impact assessment

The quantification and prediction of particulate matter (PM) concentrations in the air are essential due to their negative impacts on human health and the environment. This study quantified PM concentrations and associated health effects at four natural gas-based power plants in Bangladesh. The measurement of PM2.5 and PM10 using the respirable dust samplers APS-113NL and APS-113BL, respectively from the year of 2015 to 2021 revealed that the concentration of both types of particles fluctuated over the years. The highest recorded levels of particles were in 2019, with PM2.5 at 126 µg/m3 and PM10 at 283 µg/m3 and the lowest recorded levels of particles were in 2017, with PM2.5 at 76.3 µg/m3 and PM10 at 203.3 µg/m3. In 2021, PM2.5 and PM10 concentrations were 88.5 and 225 µg/m3, respectively, lower than in the past two years. Statistical modeling assessed atmospheric contaminants analyzed time series data, and projected air quality. ARIMA, ETS, and ANN modeling methods have been used to predict the monthly average of PM2.5 and PM10 concentrations. RMSE, MAPE, MASE, and MAE have been utilized for model orders, time series analysis, and forecasting validation. There is a significant variation between the forecasting models and forecasts for average PM2.5 and PM10 concentrations in natural gas-fired power plants from 2022 to 2024. This study also conducted a face-to-face interview with over 100 employees using a structured questionnaire to assess the health effects they are facing due to poor air quality in the power generation complex and found that 2 and 13 % of employees had respiratory and skin issues, respectively. Nonetheless, regular health checks, air filtration, and renewable energy consumption may benefit residents and the environment.

Peramalan Penjualan Produk Teralis Besi Menggunakan Metode ARIMA: Studi Kasus Bengkel Las Bale Karya

Bengkel Las Bale Karya is one of the small and medium-sized enterprises (SMEs) that produces various types of iron crafts such as seesaws, swings, staircase railings, balcony railings, spiral staircases, iron doors, grilles, fence doors, and others. This research focuses on the production of iron grille products. The research method employed in this study is the ARIMA method for forecasting over the next 12 periods. The results of this study aim to optimize iron grille production to prevent product stockpiling, ensure material availability, and enhance workplace safety.