Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM

Dave, Emmanuel; Leonardo, Albert; Jeanice, Marethia; Hanafiah, Novita

doi:10.1016/j.procs.2021.01.031

Cited by 77 publications

(42 citation statements)

References 22 publications

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“…In addition, the hybrid model can make the model work together to overcome other weaknesses ( 60 ). Thus, the hybrid model was expected to deliver more accurate predictions compared to the single model ( 61 ) as observed in the present and numerous previous studies ( 11 , 55 , 57 , 60 ).…”

Section: Discussionsupporting

confidence: 72%

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

et al. 2021

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Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.

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Section: Discussionsupporting

confidence: 72%

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

et al. 2021

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“…For the linear component of the time series they adapted ARIMA model and for the non-linear component, the LSTM model is used. In a similar way, the study proposed in Dave et al (2021) uses ARIMA and LSTM for export data of Indonesia. A hybrid model using Prophet and LSTM is proposed in Zhoul, Chenl & Ni (2020) for air quality index.…”

Section: Introductionmentioning

confidence: 99%

A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data

Arslan¹

2022

PeerJ Computer Science

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For decades, time series forecasting had many applications in various industries such as weather, financial, healthcare, business, retail, and energy consumption forecasting. An accurate prediction in these applications is a very important and also difficult task because of high sampling rates leading to monthly, daily, or even hourly data. This high-frequency property of time series data results in complexity and seasonality. Moreover, the time series data can have irregular fluctuations caused by various factors. Thus, using a single model does not result in good accuracy results. In this study, we propose an efficient forecasting framework by hybridizing the recurrent neural network model with Facebook’s Prophet to improve the forecasting performance. Seasonal-trend decomposition based on the Loess (STL) algorithm is applied to the original time series and these decomposed components are used to train our recurrent neural network for reducing the impact of these irregular patterns on final predictions. Moreover, to preserve seasonality, the original time series data is modeled with Prophet, and the output of both sub-models are merged as final prediction values. In experiments, we compared our model with state-of-art methods for real-world energy consumption data of seven countries and the proposed hybrid method demonstrates competitive results to these state-of-art methods.

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“…To overcome these limitations, the most popular solution is to use the Long Short-Term Memory (LSTM) method [32], this type of RNN can detect the most relevant information from the data and then split the time series signal between what is important in the short term, and what is in the long term [31,33], which makes it capable of exploiting forecasts over long periods of time [12,34,35], and that the LSTM is able to produce long-term forecasts because of its dependence on the past [36].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Supply Chain Management: A Deep Learning Model Using an Optimized LSTM Network for Demand Forecasting

2022

IJIES

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The covid 19 has caused a strong health and economic crisis, where the business environment is turning into a great uncertainty and customer demand has become more and more fluctuating. As a result, demand forecasting as well as the task of predictive analysis, holds a great attention in the supply chain, in order to meet the needs of customers, avoid any out of stock, and at the same time avoid a waste of resources. In this paper, we propose a deep learning method based on long-term memory multilayer networks (LSTM) for demand forecasting. Using the grid search method, our method has the ability to automatically select the most optimal forecasting model, considering different combinations of LSTM hyperparameters of a time series. The proposed model has shown strength in capturing the existing nonlinear features in time series data compared to some known time series forecasting methods derived from statistical and machine learning approaches, using historical sales data of a Moroccan pharmaceutical manufacturing company. These methods include exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), recurrent neural network (RNN). The evaluation of the proposed method and the comparison methods was performed using the root mean square error (RMSE) and Symmetric mean absolute percentage error (SMAPE). The comparison of the test results showed that the proposed method is the best performing with RMSE of 4487.32 and SMAPE of 0.026 much better than those obtained by the other models.

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Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM

Cited by 77 publications

References 22 publications

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

A hybrid forecasting model using LSTM and Prophet for energy consumption with decomposition of time series data

Machine Learning Applications in Supply Chain Management: A Deep Learning Model Using an Optimized LSTM Network for Demand Forecasting

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