The combined forecasting method of GM(1,1) with linear regression and its application

Non-oil and gas exports are one of the largest foreign exchange earners for Indonesia. Non-oil and gas exports always experience a decline in the month of Eid Al-Fitr due to delays in the delivery of export goods because the loading and unloading of goods at the port is reduced during Eid Al-Fitr. The shift of the Eid Al-Fitr month on the data will form a pattern or season with an unequal period called the moving holiday effect. The time series forecasting method that usually used the ARIMA method. Because the ARIMA method only suitable for time series data with the same seasonal period and can’t handle the moving holiday effect, the X-13-ARIMA-SEATS method used two steps. First, regARIMA modeling is a linear regression between time series data and the weight of Eid Al-Fitr and the residuals follow the ARIMA process. The weighting is based on three conditions, namely pre_holiday, post_holiday, and multiple. Second, X-12-ARIMA decomposition method for seasonal adjustments that produces trend-cycle components, seasonal, and irregular. Based on the analysis carried out on the monthly non-oil and gas export data for the period January 2013 to December 2017, the X-13-ARIMA-SEATS (1,1,0) model was obtained in the post_holiday condition as the best model. The forecasting results in 2018 show the largest decline in non-oil and gas exports in June 2018 which coincided with the Eid Al-Fitr holiday. MAPE value of 10.90% is obtained which shows that the forecasting ability is good.Keywords: time series, non-oil and gas, X-13-ARIMA-SEATS, moving holiday

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ANNUAL AVERAGE DAILY TRAFFIC FORECASTING USING DIFFERENT TECHNIQUES

Transport ◽

10.3846/16484142.2006.9638039 ◽

2006 ◽

Vol 21 (1) ◽

pp. 38-43 ◽

Cited By ~ 3

Author(s):

Tomas Šliupas

Keyword(s):

Growth Factor ◽

Linear Regression ◽

Multiple Regression ◽

Traffic Data ◽

Annual Average ◽

Traffic Forecasting ◽

Annual Average Daily Traffic ◽

Forecasting Method ◽

The Future

This paper describes annual average daily traffic (AADT) forecasting for the Lithuanian highways using a forecasting method used by Idaho Department for Transportation, growth factor, linear regression and multiple regression. AADT forecasts obtained using these methods are compared with the forecasts evaluated by traffic experts and given in references. The results show that the best Lithuanian traffic data are obtained using Idaho forecasting method. It is assumed that the curve of AADT change should be exponential in the future.

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ESTABLISHING A COMBINED FORECASTING MODEL: A CASE STUDY ON THE LOGISTIC DEMAND OF NANJING’S GREEN TEA INDUSTRY IN CHINA

Technological and Economic Development of Economy ◽

10.3846/tede.2020.14008 ◽

2020 ◽

Vol 0 (0) ◽

pp. 1-25

Author(s):

Hung-Lung Lin ◽

Chin-Tsai Lin

Keyword(s):

Green Tea ◽

Limited Data ◽

Tea Leaves ◽

Use Of Time ◽

Tea Industry ◽

Forecasting Method ◽

Logistics Demand ◽

Combined Forecasting ◽

The Ideal

The sales logistics of tea leaves is a process that organically integrates basic logistics activities, including transportation, storage, loading, unloading, carrying, packaging, distribution processing, delivery, and information processing. This process requires quick and accurate forecasting of the logistics demand in the green tea market and the provision of feedback to businesses and farming partners, revealing the need for a simple and accurate forecasting method. Responding to and solving the unclear information and limited data available regarding the green tea market are critical. Therefore, this study established a simple, quick, and accurate model through the use of time series and the technique for ordering preferences by similarity to the ideal solution. Finally, the actual logistics demand in the Nanjing green tea industry was employed to verify the proposed model’s practicality and feasibility, which may provide a critical reference for relevant parties such as businesses and researchers.

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