Long-term inference based on short-term forecasting models

1993 ◽  
pp. 9-25 ◽  
Author(s):  
P. Newbold ◽  
C. Agiakloglou ◽  
J. Miller
Keyword(s):  
Short Term ◽  
Forecasting Models ◽  
Short Term Forecasting

scholarly journals APPLICATION OF SHORT-TERM FORECASTING MODELS FOR ENERGY ENTITY STOCK PRICE (STUDY ON INDIKA ENERGI TBK, JII)

2020 ◽  
Vol 10 (1) ◽  
pp. 294-301
Author(s):  
Rialdi Azhar ◽  
Fajrin Satria Dwi Kesumah ◽  
Ambya Ambya ◽  
Febryan Kusuma Wisnu ◽  
Edwin Russel
Keyword(s):  
Stock Price ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Forecasting

Short-Term Forecasting Method of Maximum Usable Frequency for HF Communication Based on Oblique Sounder

2013 ◽  
Vol 336-338 ◽  
pp. 1676-1681
Author(s):  
Jin Hai Hou ◽  
Jian Wang ◽  
Li Qiang Wu ◽  
Sheng Yun Ji ◽  
Dan Jun Chen
Keyword(s):  
Critical Frequency ◽  
Short Term ◽  
System A ◽  
Maximum Usable Frequency ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Frequency Management ◽  
Ray Path ◽  
Long Term Prediction

Based on oblique sounding system, a short-term forecasting method of maximum usable frequency for HF communication was presented. The method include four steps: firstly, maximum usable frequency of oblique sounding circuit was forecasted based on its sounding data; secondly, the critical frequency of sounding path midpoint was determined from the oblique sounding data and its forecast data by using ray-path theory; thirdly, the critical frequency was reconstructed for forecast circuit; in the end, maximum usable frequency for HF communication circuit was given. Using the oblique sounding data during March 2012, the accuracy and practicability of the method was validated. The root-mean-square error of the short-term forecast is 1.56 MHz, and is reduced 0.40MHz contrast to that of the long-term prediction. The research provides the important reference for frequency selection and frequency management of HF communication.

scholarly journals A Multivariate Multi-Step LSTM Forecasting Model For Tuberculosis Incidence With Model Explanation In Liaoning Province, China

2022 ◽  
Author(s):  
Enbin Yang ◽  
Hao Zhang ◽  
Xinsheng Guo ◽  
Zinan Zang ◽  
Zhen Liu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Liaoning Province ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Short Term ◽  
Forecasting Models ◽  
Absolute Percentage Error

Abstract Background: In addition to COVID-19, tuberculosis (TB) is the respiratory infectious disease with the highest incidence in China. We aim to design a series of forecasting models and find the factors that affect the incidence of TB, thereby improving the accuracy of the incidence prediction. Results: In this paper, we developed a new interpretable prediction system based on the multivariate multi-step Long Short-Term Memory (LSTM) model and SHapley Additive exPlanation (SHAP) method. Moreover, four accuracy measures are introduced into the system: Root Mean Square Error, Mean Absolute Error, Mean Absolute Percentage Error, and symmetric Mean Absolute Percentage Error. Meanwhile, the Autoregressive Integrated Moving Average (ARIMA) model and seasonal ARIMA model are established. The multi-step ARIMA-LSTM model is proposed for the first time to examine the performance of each model in the short, medium, and long term, respectively. Compared with the ARIMA model, each error of the multivariate 2-step LSTM model is reduced by 12.92%, 15.94%, 15.97%, and 14.81% in the short term. The 3-step ARIMA-LSTM model achieved excellent performance, with each error decreased to 15.19%, 33.14%, 36.79%, and 29.76% in the medium and long term. We provide the local and global explanation of the multivariate single-step LSTM model in the field of incidence prediction, pioneering. Conclusions: The multivariate 2-step LSTM model is suitable for short-term forecasts, and the 3-step ARIMA-LSTM model is appropriate for medium and long-term forecasts. In addition, the prediction effect was better than similar TB incidence forecasting models. The SHAP results indicate that the five most crucial features are maximum temperature, average relative humidity, local financial budget, monthly sunshine percentage, and sunshine hours.

Defining the Long-Term Wave Resource at Wave Hub: The Role of Measurements and Models

2008 ◽  
Author(s):  
Joseph Phillips ◽  
Joao Cruz ◽  
Rob Holbrow ◽  
Jeremy Parkes ◽  
Rob Rawlinson-Smith
Keyword(s):  
Wave Energy ◽  
Resource Assessment ◽  
Wave Climate ◽  
Short Term ◽  
Wave Data ◽  
Wave Measurements ◽  
Operations And Maintenance ◽  
South West Coast ◽  
Short Term Forecasting

Wave measurements made on the site of a potential wave energy project can be of high value to developers. Such data can be used to define both long-term and short-term wave energy resources available to devices as well as the optimal operations and maintenance strategy which should be employed for the project. All three of these applications are addressed in an ongoing study commissioned by the npower Juice Fund for the Wave Hub project which is planned off the South West coast of England. The aim of this work is to extract best value from the historical and future wave measurements from the project site. The programme of this project is outlined here with a technical description of activity in the three parallel strands of the study; wave resource assessment, short-term forecasting and O&M modelling. The focus of this paper is on a key aspect of the ongoing work programme - that relates to the use of measured and modelled wave data to derive a prediction of the long-term wave climate at the Wave Hub site. In particular, various candidate methodologies for correlating short-term measured wave data and long-term modelled data are explored in the context of a Measure-Correlate-Predict (MCP) analysis. This work has also included consideration of the inter-annual variability of wave resource in order to examine the uncertainty associated with assuming that a finite historical reference period is representative of the long-term wave climate.

scholarly journals ASSESSING THE FORECASTING PERFORMANCE OF ARIMA AND ARIMAX MODELS OF RESIDENTIAL PRICES IN ABUJA NIGERIA

2019 ◽  
Vol 4 (1) ◽  
pp. 4-6
Author(s):  
MUSA ABUBAKAR ALKALI
Keyword(s):  
Household Income ◽  
Crude Oil Price ◽  
Short Term ◽  
Macroeconomic Factors ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Short Term Forecasting ◽  
Year 2000 ◽  
Domestic Products

This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from  the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX  forecasting models, with macroeconomic factors as exogenous variables  such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.

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