The Prediction Of Export Product Prices With Holt’s Double Exponential Smoothing Method

Maulana, Hata; Mulyantika, Ulfa

doi:10.1109/ic2ie50715.2020.9274679

Cited by 9 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, data mining, machine learning and statistics play a huge role in extracting knowledge and predicting the future price of the commodities precisely [10,11]. Several statistical models are available in the literature that can be used in forecasting time series data, among which the most popular and widely used techniques are autoregressive moving average (ARMA) [12], autoregressive integrated moving average (ARIMA) [13], simple exponential smoothing (SES) [14], naïve forecasting (NF) [15], Holt's method [16] and more. Many machine learning algorithms, such as linear regression (LR) [17], Gaussian process (GP) [18], neural networks [19], support vector machines (SVM) [20], multilayer perceptron (MP), and fuzzy logic [21], are also widely used in forecasting prices.…”

Section: *Author For Correspondencementioning

confidence: 99%

“…Result comparison of proposed model with other machine learning algorithmsFigure16 MSE for coconut price prediction at various markets in Coimbatore Similarly, to the price of the coconut, the price of various other commodities is extracted from various markets in and around the Coimbatore district via the website agmarknet.gov.in. The models used in the proposed work are executed for the various datasets that contain the prices of the commodities from the various markets in Coimbatore, such as turmeric at the Coimbatore market, cotton at the Tirupur market, maize at the Sulur market, tobacco at the Palladam market, groundnut at the Sevur market, and paddy at the Udumalaipet market.…”

mentioning

confidence: 99%

See 1 more Smart Citation

An extrapolative model for price prediction of crops using hybrid ensemble learning techniques

2023

IJATEE

View full text Add to dashboard Cite

Agriculture is the basis for food and the backbone of a country's economy. In India, around 70% of the population is actively involved in growing crops for food or providing direct raw materials to a variety of industries, including textile, food processing, and non-agricultural sectors. The development of technology aids the agricultural sector in forecasting a variety of factors, including crop quality, disease detection, and soil quality, to increase crop yield. However, increased agricultural yield may not always result in a profit due to price reductions. Thus, price forecasting is crucial before choosing the crop to plant since it aids in making informed choices that reduce the risk and loss associated with market price instabilities. This study provides a hybrid model for price prediction that combines an autoregressive integrated moving average (ARIMA) model, a linear statistical analysis for time series data, and an ensemble machine learning approach using support vector regression (SVR). The work has three models: 1) a statistical model is applied over the input features related to crop price, and the residuals are evaluated using SVR, 2) SVR is applied over the predicted price from the statistical model along with the other input features, 3) SVR is applied to the results obtained from the statistical model and its residuals, in addition to the input features. After analysing the results, the model for price forecasting that produced better outcomes was finally chosen. The experimental and result analysis reveals that model 3 has improvedresults with a 13.37% deviation from actual observation compared to models 1 and 2, which have resulted with deviations of 14.68% and 16.48%, respectively. Additionally, compared to other models, the suggested model has the lowest average prediction errors and average divergence from actual values. Thus, the proposed model is suitable for reliable price forecasting and optimal performance.

show abstract

Section: *Author For Correspondencementioning

confidence: 99%

mentioning

confidence: 99%

An extrapolative model for price prediction of crops using hybrid ensemble learning techniques

2023

IJATEE

View full text Add to dashboard Cite

show abstract

“…Metode Double Exponential Smoothing Holt sangat sesuai pada data yang menunjukkan pola tren [5]. Metode Holt memiliki dua parameter alpa dan beta dengan proses pemulusan level dan pemulusan tren [6]. Nilai parameter tersebut diperoleh dengan perulangan untuk menemukan kombinasi terbaik yang menghasilkan nilai error terkecil.…”

Section: Pendahuluanunclassified

Untitled

Maulidiyah

2023

jik

View full text Add to dashboard Cite

Stocks are one of the capital market transactions that are the choice of investors and traders. Investors and traders need strategies to profit from stocks to reduce losses. One of the predictions of future stock price movements is by using the time series method approach. DES Holt method is a stock price prediction method from time series data. However, Holt's DES method has the problem of overcasting, which needs to be mitigated by using Damped Trend. This study uses DES Holt and Damped Trend to determine the effect of Damped Trend in reducing trend forecasting. DES Holt's application uses two parameters, namely alpha and beta. Damped Trend uses three parameters, namely alpha, beta, and phi, which are performed on four stock data. The results of the study using the DES Holt method on the TLKM dataset achieved an accuracy value of 91.1693%, the MDKA dataset achieved an accuracy of 84.9775%, the HMSP dataset had an accuracy value of 92.2785%, the KLBF dataset achieved an accuracy of 92.5582%. The results of the study using Damped Trend on the TLKM dataset achieved an accuracy of 96.3035%, the MDKA dataset had an accuracy of 94.4714%, the HMSP dataset had an accuracy of 92.4587%, and the KLBF dataset had an accuracy of 98.0674%. The results of the comparison show that the Damped Trend method is able to increase the accuracy of the four datasets. The increase in the accuracy of the four datasets for TLKM data was 5.1342%, MDKA 9.4938%, and HMSP 0.1802% and the increase in KLBF accuracy was 5.5092%.

show abstract

“…Penelitian sebelumnya yang menggabungkan metode Double Exponential Smoothing dengan optimasi parameter Golden Section dilakukan pada peramalan Nilai Tukar Petani (NTPT) di Provinsi Kalimantan Timur, dan hasilnya menunjukkan hasil MAPE sebesar 0,8856663%, menunjukkan tingkat peramalan yang sangat baik. akurasi (Anggre et al, 2022), Studi lain tentang peramalan harga produk ekspor juga mencapai nilai MAPE sebesar 0,20%, menunjukkan tingkat peramalan yang sangat baik Berdasarkan penelitian sebelumnya, penelitian ini akan melakukan peramalan CPI di Indonesia menggunakan Double Exponential Smoothing Holt dan optimasi parameter dengan Golden Section metode (Maulana and Mulyantika, 2020).…”

Section: Pendahuluanunclassified

Optimasi Golden Section pada Metode Double Exponential Smoothing untuk Meramalkan Indeks Harga Konsumen di Indonesia

Zumrotul Muallifah,

Wika Dianita Utami,

Hani Khaulasari

et al. 2023

statistika

View full text Add to dashboard Cite

ABSTRAK Indeks Harga Konsumen (IHK) dapat digunakan sebagai indikator ekonomi dan ukuran tingkat biaya produksi, serta berguna dalam memantau tingkat kenaikan harga dan pendapatan. Pemerintah perlu secara berkala menyesuaikan kebijakan inflasi IHK untuk menjaga stabilitas situasi ekonomi rakyat, mengingat tingkat inflasi IHK berubah setiap bulan. Penelitian ini bertujuan untuk mengoptimalkan parameter dan melakukan peramalan IHK untuk periode November 2022 hingga Oktober 2023 menggunakan metode Double Exponential Smoothing Holt dengan optimasi parameter menggunakan metode Golden Section. Hasil penelitian menunjukkan bahwa MAPE (Mean Absolute Percentage Error) sebesar 0,8140406%, dengan nilai parameter optimal sebesar 0,9957141 dan 0,0127842. Peramalan menunjukkan tingkat stabilitas yang baik dari bulan November 2022 hingga Oktober 2023. ABSTRACT The Consumer Price Index (CPI) can be used as an economic index and a measure of production costs, and it is useful for examining price increases and income levels. The government needs to periodically adjust CPI inflation policies to ensure the stability of the people's economy since the CPI inflation rate changes every month. The research aims to obtain optimized parameter results and forecasts for the CPI from November 2022 to October 2023 using the Golden Section parameter optimization in Double Exponential Smoothing Holt. The research results using the Double Exponential Smoothing Holt method and Golden Section parameter optimization show an MAPE value of 0.8140406% and parameter values of 0.9957141 and 0.0127842. The forecasted results indicate a stable trend from November 2022 to October 2023.

show abstract

The Prediction Of Export Product Prices With Holt’s Double Exponential Smoothing Method

Cited by 9 publications

References 7 publications

An extrapolative model for price prediction of crops using hybrid ensemble learning techniques

An extrapolative model for price prediction of crops using hybrid ensemble learning techniques

Untitled

Optimasi Golden Section pada Metode Double Exponential Smoothing untuk Meramalkan Indeks Harga Konsumen di Indonesia

Contact Info

Product

Resources

About