The Treatment of Financial Variables in Social Accounting Matrix-Based Short-Term Forecasting Models

2005 ◽  
Vol 26 (2) ◽  
Author(s):  
Jean K Thisen
Keyword(s):  
Social Accounting Matrix ◽  
Short Term ◽  
Social Accounting ◽  
Financial Variables ◽  
Forecasting Models ◽  
Short Term Forecasting

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

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.

scholarly journals Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 11 ◽  
Author(s):  
María Carmen Ruiz-Abellón ◽  
Luis Alfredo Fernández-Jiménez ◽  
Antonio Guillamón ◽  
Alberto Falces ◽  
Ana García-Garre ◽  
...  
Keyword(s):  
Power Systems ◽  
Demand Response ◽  
Time Series Data ◽  
Load Forecasting ◽  
Real Data ◽  
Series Data ◽  
Renewable Generation ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Forecasting

The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure.

scholarly journals The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3671
Author(s):  
Junxia Jiang ◽  
Qingquan Lv ◽  
Xiaoqing Gao
Keyword(s):  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Solar Irradiation ◽  
Short Term ◽  
Clear Sky ◽  
Forecasting Models ◽  
Forecasting Performance ◽  
Sky Imager ◽  
Short Term Forecasting ◽  
Sky Conditions

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.

scholarly journals Verification and validation of developed short-term forecasting models

2021 ◽  
Author(s):  
Cody Walker ◽  
Vivek Agarwal ◽  
Nancy Lybeck ◽  
Mike Taylor ◽  
Pradeep Ramuhalli
Keyword(s):  
Verification And Validation ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Forecasting

scholarly journals Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

2020 ◽  
Vol 11 (1) ◽  
pp. 73-88
Author(s):  
Paweł Baranowski ◽  
Karol Korczak ◽  
Jarosław Zając
Keyword(s):  
Regression Models ◽  
Linear Regression Models ◽  
Short Term ◽  
Research Attention ◽  
Forecasting Models ◽  
Box Office ◽  
Out Of Sample ◽  
Short Term Forecasting ◽  
The Individual ◽  
Weekly Frequency

AbstractBackground: Cinema programmes are set in advance (usually with a weekly frequency), which motivates us to investigate the short-term forecasting of attendance. In the literature on the cinema industry, the issue of attendance forecasting has gained less research attention compared to modelling the aggregate performance of movies. Furthermore, unlike most existing studies, we use data on attendance at the individual show level (179,103 shows) rather than aggregate box office sales.Objectives: In the paper, we evaluate short-term forecasting models of cinema attendance. The main purpose of the study is to find the factors that are useful in forecasting cinema attendance at the individual show level (i.e., the number of tickets sold for a particular movie, time and cinema).Methods/Approach: We apply several linear regression models, estimated for each recursive sample, to produce one-week ahead forecasts of the attendance. We then rank the models based on the out-of-sample fit.Results: The results show that the best performing models are those that include cinema- and region-specific variables, in addition to movie parameters (e.g., genre, age classification) or title popularity.Conclusions: Regression models using a wide set of variables (cinema- and region-specific variables, movie features, title popularity) may be successfully applied for predicting individual cinema shows attendance in Poland.

scholarly journals Comparative Study of Short-term Forecasting Models of Online Car-hailing Demand

2020 ◽  
Vol 1646 ◽  
pp. 012097
Author(s):  
Shiyu Chang ◽  
Zhili Liu ◽  
Dongyu Kuang ◽  
Hui Zhao
Keyword(s):  
Comparative Study ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Forecasting
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