The Long-Term Forecasting Method for IFTS

2019 ◽  
Author(s):  
Sha-Sha Xu ◽  
Kou-Quan Zheng
Keyword(s):  
Forecasting Method ◽  
Long Term Forecasting

scholarly journals Comparison of the long-term forecasting method of RSSI by machine learning

2020 ◽  
Vol 9 (11) ◽  
pp. 553-558
Author(s):  
Tatsuya Nagao ◽  
Takahiro Hayashi ◽  
Yoshiaki Amano
Keyword(s):  
Machine Learning ◽  
Forecasting Method ◽  
Long Term Forecasting

scholarly journals LONG-TERM FORECASTING OF EXTRAORDINARY SPRING FLOODS BY СOMMENSURABILITY METHOD ON THE DNIPRO RIVER NEAR THE KYIV CITY, UKRAINE

2019 ◽  
Vol 75 (2) ◽  
pp. 74-81
Author(s):  
Borys Fedorovich Khrystiuk ◽  
Liudmyla Olexandrivna Gorbachova
Keyword(s):  
Lead Time ◽  
Quantitative Methods ◽  
Practical Significance ◽  
The Past ◽  
Forecasting Method ◽  
Forecast Lead Time ◽  
Information Method ◽  
Long Term Forecasting ◽  
Industrial Center

The Kyiv city is the capital of Ukraine, as well as its major administrative and industrial center. Kyiv is located in the middle reaches of the Dnipro River which is the largest river in Ukraine. In the past, the Kyiv city suffered from dangerous spring floods. Consequently, long-term forecasting of spring floods on the Dnipro River near Kyiv has an important scientific and practical significance. Existing quantitative methods for such forecasting are of limited forecast lead time and require many input hydrometeorological data. In the paper the information method Weng Wen-Bo applied, which is a qualitative forecasting method. The use such method allows to determine the periods and specific years in which the following extraordinary spring floods on the Dnipro River near Kyiv can occur.

scholarly journals Comparison of Forecasting Techniques for Short-term and Long-term Real Life Problems

2017 ◽  
Vol 65 (2) ◽  
pp. 139-144
Author(s):  
Nandita Barman ◽  
M Babul Hasan
Keyword(s):  
Time Series ◽  
Real Life ◽  
Exponential Smoothing ◽  
Short Term ◽  
Life Problems ◽  
Forecasting Method ◽  
Out Of Sample ◽  
Long Term Forecasting ◽  
Exponential Smoothing Technique

In this paper, we analyze the most appropriate short-term and long term forecasting methods for our practical life where several methods of time series forecasting are available such as the Moving Averages method, Linear Regression with Time, Exponential Smoothing, Holt‘s Method, Holt-Winter‘s Method etc. This paper mainly concentrates on the Holt- Winters Exponential Smoothing technique as applied to time series that exhibit seasonality. The accuracy of the out-of-sample forecast is measured using MSE, MAPE, MAD. We will observe that the empirical results from the study indicate that the Holt-Winter‘s Multiplicative Forecasting Method processes as the most appropriate forecasting method for the sets of real life data that will be analyzed. Dhaka Univ. J. Sci. 65(2): 139-144, 2017 (July)

scholarly journals Long-Term Sales Forecasting Using Lee-Carter And Holt-Winters Methods

2011 ◽  
Vol 27 (1) ◽  
Author(s):  
Wassana Suwanvijit ◽  
Thomas Lumley ◽  
Chamnein Choonpradub ◽  
Nittaya McNeil
Keyword(s):  
Statistical Model ◽  
Exponential Smoothing ◽  
Actual Data ◽  
Font Size ◽  
Mortality Forecasting ◽  
Forecasting Method ◽  
Southern Thailand ◽  
Long Term Forecasting

<p style="text-justify: inter-cluster; text-align: justify; margin: 0in 0.5in 0pt; background: white; mso-pagination: none;"><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; color: black; font-size: 10pt; mso-themecolor: text1; mso-bidi-font-style: italic;">This study developed a statistical model for long-term forecasting sparkling beverage sales in the 14 provinces of Southern Thailand. Data comprised the series of monthly sales from January 2000 to December 2004 obtained from the company. We applied a classical Lee-Carter </span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; color: black; font-size: 10pt; mso-themecolor: text1; mso-fareast-font-family: 'MS Mincho'; mso-fareast-language: JA; mso-bidi-font-style: italic;">mortality forecasting </span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; color: black; font-size: 10pt; mso-themecolor: text1; mso-bidi-font-style: italic;">approach as well as exponential smoothing Holt-Winters with additive seasonality method to log-transformed monthly sales containing season of month and branch location as factors.<span style="mso-spacerun: yes;">&nbsp; </span>The model produced excellent estimates in sales predicting for up to 24 future months of 20 branches compared with actual data in each branch during the years 2005-2006. The model also gave more accurate results than using separate forecasting method whereas it was parsimonious in the number of parameters used. </span></p>

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