scholarly journals Time series modeling by using exponential smoothing technique for river flow discharge forecasting (case study: Cabenge, Walanae, and Cenranae rivers system)

2021 ◽  
Vol 1088 (1) ◽  
pp. 012100
Author(s):  
Melly Lukman ◽  
Benyamin Tanan
Keyword(s):  
Time Series ◽  
River Flow ◽  
Exponential Smoothing ◽  
Time Series Modeling ◽  
Smoothing Technique ◽  
Flow Discharge ◽  
Exponential Smoothing Technique

scholarly journals Forecasting of Palm Oil Fruit Delivery Quantities Using the Trend Adjusted Exponential Smoothing: A Case Study

2012 ◽  
Vol 488-489 ◽  
pp. 1277-1281
Author(s):  
Noor Ajian Mohd-Lair ◽  
Abdul Halim Kudi ◽  
Bih Lii Chua ◽  
Rosalam Sarbatly
Keyword(s):  
Palm Oil ◽  
Method Comparison ◽  
Oil Industry ◽  
Exponential Smoothing ◽  
Smoothing Technique ◽  
Naive Method ◽  
Palm Oil Mill ◽  
Forecast Technique ◽  
Exponential Smoothing Technique

Palm oil industry has increasingly become the important industry for Malaysia. However, only a limited number of researches have been conducted on improving the palm oil industries. This research attempted to contribute by improving forecast activity along the palm oil industry. Specifically, this research centred on the development of forecast software for a Malaysian based palm oil estate. The developed forecasting software can be used to assist the estate manager in predicting accurately their monthly delivery quantity to the palm oil mill. The forecast technique selected for this research was the trend adjusted exponential smoothing technique. The performance of the trend adjusted exponential smoothing technique based software was then compared to the naïve method. Comparison in the performance indicated that the trend adjusted exponential smoothing produces lower root mean square error, which is equivalent to 14.6% of error produced by the naïve method. This finding emphasises the efficiency of the trend adjusted exponential smoothing in predicting the monthly delivery quantity by the palm oil estate.

scholarly journals Forecasting Malaysian Ringgit Using Exponential Smoothing Techniques

2021 ◽  
Vol 6 (2) ◽  
pp. 1-10
Author(s):  
Noreha Mohamed Yusof ◽  
Norani Amit ◽  
Nor Faradilah Mahad ◽  
Noorezatty Mohd Yusop
Keyword(s):  
Exchange Rate ◽  
Foreign Currency ◽  
The United States ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Smoothing Technique ◽  
Smoothing Techniques ◽  
United States Dollar ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Technique

Forecasting the foreign currency exchange is a challenging task since it is influenced by political, economic and psychological factors. This paper focuses on the forecasting Malaysian Ringgit (MYR) exchange rate against the United States Dollar (USD) using Exponential Smoothing Techniques which are Single Exponential Smoothing, Double Exponential Smoothing, and Holt’s method. The objectives of this paper are to identify the best Exponential Smoothing Technique that describes MYR for 5 years period and to forecast MYR 12 months ahead by using the best Exponential Smoothing Technique. The comparison between these techniques is also made and the best one will be selected to forecast the MYR exchange rate against USD. The result showed that Holt’s method has the smallest value of error measure which depending on the Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) for the evaluation part. The MSE is 1.43915x10-14 and MAPE is 2.5413 x 10-6. Meanwhile, the forecast value of MYR in August 2019 is RM 4.30226.

scholarly journals VISUALISASI PREDIKSI KUNJUNGAN WISATAWAN MANCANEGARA MENGGUNAKAN MODEL TIME SERIES

JOUTICA ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 356
Author(s):  
Ruli Utami ◽  
Mohammad Whildan Indra Maulana
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Time Series Modeling ◽  
Model Time Series

Sektor pariwisata merupakan sektor dengan prospek tinggi untuk meningkatkan pendapatan negara melalui penerimaan Devisa. Selain itu adanya wisatawan ini juga sangat berdampak pada ekonomi kecil warga sekitar tempat wisata, sehingga sektor ini harus dikelolah dengan bijaksanan. Daya tarik wisata Indonesia sangat menarik minat dari wisatawan mancanegara untuk berkunjung ke Indoensia, hal ini harus sebanding dengan pelayanan publik yang disediakan untuk wisatawan ini; misalnya sarana dan fasilitas hotel serta layanan lain seperti imigrasi. Hal ini dapat dilakukan jika pihak yang berwenang dapat memprediksi jumlah kunjungan wisatawan masing-masing negara. Dari permasalahan tersebut diatas, maka dibuatlah sebuah aplikasi yang bertujuan untuk menampilakn visualisasi prediksi yang dihitung menggunakan pemodelan menggunakan time series modeling dengan visualisasi hasil prediksi melalui sebuah aplikasi. Dari penelitian yang telah dilakukan dengan metode exponential smoothing dapat disimpulkan bahwa nilai parameter yang paling cocok digunakan adalah nilai α = 0.6 dengan nilai MAPE 6.77%.

A Method for Scalable Real-Time Network Performance Baselining, Anomaly Detection, and Forecasting

2012 ◽  
Vol 3 (2) ◽  
pp. 13-33
Author(s):  
Robert Strahan
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Performance Management ◽  
Network Performance ◽  
Anomalous Behavior ◽  
Management Systems ◽  
Smoothing Technique ◽  
Performance Management Systems ◽  
Exponential Smoothing Technique ◽  
Network Metrics

Communication is the lifeblood of any business. Today, communication is predominantly facilitated by digital packets transported over the interconnected arteries of the data network infrastructure. It is imperative that this infrastructure is well managed, that unexpected behavior is quickly identified and explained, and that problems are predicted and preempted. Therefore, network performance management systems should be able to detect unusual or anomalous behavior as it happens, and quickly trigger automatic analysis or alert a human operator. Growth trends in network traffic must also be identified so that future problems may be anticipated and prevented. To meet these challenges, this paper proposes an integrated, scalable method to perform baselining, anomaly detection, and forecasting on time series network metrics. The method is based on the popular Holt-Winters triple exponential smoothing technique – a technique that compares favorably to other more complex and costly approaches.

scholarly journals Forecasting Basis Levels in the Soybean Complex: A Comparison of Time Series Methods

2006 ◽  
Vol 38 (3) ◽  
pp. 513-523 ◽  
Author(s):  
Dwight R. Sanders ◽  
Mark R. Manfredo
Keyword(s):  
Time Series ◽  
Soybean Oil ◽  
Soybean Meal ◽  
Vector Autoregression ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Forecast Evaluation ◽  
Time Series Modeling ◽  
Var Models ◽  
Smoothing Techniques

A battery of time series methods are compared for forecasting basis levels in the soybean futures complex: soybeans, soybean meal, and soybean oil. Specifically, nearby basis forecasts are generated with exponential smoothing techniques, autoregression moving average (ARMA), and vector autoregression (VAR) models. The forecasts are compared to those of the 5-year average, year ago, and no change methods. Using the 5-year average as the benchmark method, the forecast evaluation results suggest that alternative naive techniques may produce better forecasts, and the improvement gained by time series modeling is relatively small. In this sample, there is little evidence that the basis has become systematically more difficult to forecast in recent years.

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