A Comparative Study of ARIMA and Holt-Winters Exponential Smoothing Models for Rice Price Forecasting in Tanzania

Mgale, Yohana James; Yan, Yunxian; Timothy, Shauri

doi:10.4236/oalib.1107381

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…Holt-Winters exponential smoothing model can either be a multiplicative or additive model. The choice of the model depends on the seasonal component of the series (Mgale, 2021): a) In Holt-Winters additive method, seasonal pattern of a series has a constant amplitude.…”

Section: Additive Hw Methodsmentioning

confidence: 99%

“…Depending on the type of seasonality, it can either be additive or multiplicative. According to Mgale et al (2021), forecasts will depend on three components of seasonal time series: its level, its trend and its seasonal coefficient. Exponential smoothing is a method to smooth a time series where it allocates exponentially decreasing weights and values in opposition to historical data to lessen the value of the weights for the bygone data.…”

Section: Literature Reviewmentioning

confidence: 99%

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Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data

Pleños

2022

PRS

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This study utilized the data on abaca fiber production and used Holt-Winters model to forecast the abaca fiber production since the studied variable is characterized by a fairly strong intensity of seasonality. For the construction of forecasts, additive and multiplicative models were used. The most accurate forecasts were selected on the basis of Mean Square Error, Root Mean Square Error, Mean Absolute Percentage Error, and Mean Absolute Scaled Error. It was found that the multiplicative method had a higher accuracy, hence it was utilized to forecast the production for the next three years. According to the findings, the anticipated fiber production for 2021-2023 showed an increase up to the second quarter, but then declining afterwards.

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Section: Additive Hw Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data

Pleños

2022

PRS

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“…Bhardwaj et al (2020) employed the NNAR model and classical time series approaches such as double moving average method and exponential smoothing to forecast the rice yield in Karnal, Haryana. Their paper found the NNAR model to be the most suitable [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach

Annamalai

Johnson

2023

SN COMPUT. SCI.

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This study uses three distinct models to analyse a univariate time series of data: Holt's exponential smoothing model, the autoregressive integrated moving average (ARIMA) model, and the neural network autoregression (NNAR) model. The effectiveness of each model is assessed using in-sample forecasts and accuracy metrics, including mean absolute percentage error, mean absolute square error, and root mean square log error. The area under cultivation in India for the following 5 years is predicted using the model whose fitted values are most like the observed values. This is determined by performing a residual analysis. The time series data used for the study was initially found to be non-stationary. It is then transformed into stationary data using differencing before the models can be used for analysis and prediction.

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“…A primary goal of this field (TS-forecasting) is to furnish a reliable forecast by mining the inherent structure and hidden information of the TS and applying it to the model appropriately. Recent works suggest that in TS-forecasting, there exist many approaches, e.g., exponential smoothing (ES) [2,3], auto-regressive integrated moving average (ARIMA) [4,5] artificial neural network (ANN) [6,7], extreme learning machine (ELM) [8,9], facebook-prophet (FB-Prophet) [10,11], support vector regression (SVR) [12,13]. Researchers have also applied ensemble approaches for TS-forecasting and realized effective forecasting, as apparent from the pieces of literature [14−16].…”

Section: *Author For Correspondencementioning

confidence: 99%

Forecasting non-linear macroeconomic indexes of India: an ensemble of MLP and Holt’s linear methods

2022

IJATEE

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Electricity and electric-equipment play a critical role in modern living, and precise price forecasting for these items aids decision-makers in anticipating changes, planning, and budgeting ahead of time. This present research focused on the wholesale price indexes of items from the "manufacture of electrical equipment (MEEQ)" group of India's existing wholesale price index (WPI) series. This work proposed a novel, state-of-art ensemble forecast approach that used multilayer perceptron (MLP) and Holt's linear (HL) approaches for some specified WPIs from this group. The researchers selected the WPIs that manifest non-linearity as determined by the curve-fit technique. The paper applied a variance-based weighted average scheme to generate the ensemble forecast. The statistical rigor-based curve-fit aided in identifying that seventeen out of forty-eight indexes manifest non-linear fits. The proposed MLP-HL ensemble approach exhibited excellent forecast results (relative root mean squared error, i.e., RRMSE < 10%) for all seventeen WPIs. For each of these seventeen WPI's, the current work compared the proposed MLP-HL with nineteen models (eight statistical, four machine learning, and seven contemporary ensemble strategies). In the majority of the cases, it outran the nineteen models in terms of mean absolute error (MAE) and mean absolute percentage error (MAPE). As an alternative pathway for forecasting these seventeen non-linear WPIs, the present work suggests using this proposed MLP-HL approach.

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A Comparative Study of ARIMA and Holt-Winters Exponential Smoothing Models for Rice Price Forecasting in Tanzania

Cited by 8 publications

References 9 publications

Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data

Time Series Forecasting Using Holt-Winters Exponential Smoothing: Application to Abaca Fiber Data

Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach

Forecasting non-linear macroeconomic indexes of India: an ensemble of MLP and Holt’s linear methods

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