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1
MENDES, EVANDRO LUIZ. « INTERVENTION MODELS TO FORECAST MONTHLY DEMAND OF ELETRIC ENERGY, CONSIDERING THE RATIONING SCENERY ». PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3336@1.
Texte intégralRésumé :
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICONesta dissertação é desenvolvida uma metodologia para previsão de demanda mensal de energia elétrica considerando cenários de racionamento. A metodologia usada consiste em, a partir das taxas de crescimento da série temporal, identificar e eliminar os efeitos do racionamento de energia elétrica através da aplicação de Modelos Lineares Dinâmicos. São analisadas também estruturas de intervenção nos modelos estatísticos de Box & Jenkins e Holt & Winters. Os modelos são então comparados segundo alguns critérios, basicamente no que tange à sua eficiência preditiva. Conclui-se ao final sobre a eficiência da metodologia proposta, dado a grande dificuldade para solucionar o problema a partir dos modelos estatísticos de Box & Jenkins e Holt & Winters. Esta solução é então proposta como a mais viável para criar cenários de racionamento e pósracionamento de energia para ser utilizado por agentes do sistema elétrico nacional.
In this dissertation, a methodology is developed to forecast monthly demand of electric energy, considering the rationing scenery. The methodology is based on, taking the growth rate from the time series, identify and eliminate the effects of electric energy rationing, using Dynamic Linear Models. It is also analyzed intervention structures in the statistics models of Box & Jenkins and Holt & Winters. The models are compared according to some criterions, mainly forecast accuracy. At the end, we concluded that the methodology proposed is more efficient, due to the difficult to solve the problem using the statistics models with intervention. This solution is proposed as the best among them to create scenery during the energy rationing and after energy rationing, to be used by the national electric system agents.
2
Robertson, Fredrik, et Max Wallin. « Forecasting monthly air passenger flows from Sweden : Evaluating forecast performance using the Airline model as benchmark ». Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242764.
Texte intégralRésumé :
In this paper two different models for forecasting the number of monthly departing passengers from Sweden to any international destination are developed and compared. The Swedish transport agency produces forecasts on a yearly basis, where net export is the only explanatory variable controlled for in the latest report. More profound studies have shown a relevance of controlling for variables such as unemployment rate, oil price and exchange rates. Due to the high seasonality within passenger flows, these forecasts are based on monthly or quarterly data. This paper shows that a seasonal autoregressive integrated moving average model with exogenous input outperforms the benchmark model forecast in seven out of nine months. Thus, controlling for oil price, the SEK/EUR exchange rate and the occurrence of Easter reduces the mean absolute percentage error of the forecasts from 3,27 to 2,83 % on Swedish data.
3
RENDINA, Cristian. « STUDY OF THE IMPACT OF MODELLING SEA SURFACE TEMPERATURE IN A MONTHLY ATMOSPHERIC ENSEMBLE PREDICTION SYSTEM ». Doctoral thesis, Università degli studi di Ferrara, 2012. http://hdl.handle.net/11392/2389449.
Texte intégralRésumé :
Atmospheric monthly forecast is intermediate between medium range forecast, an initial value
problem, and seasonal forecast, a boundary value problem. The influence of sea surface temperature
(SST) on the atmospheric dynamics in the time range of 10-40 days is still not well understood. As a
consequence, there is no common approach for the representation of the SST in a monthly prediction
system. At ISAC-CNR in Bologna a monthly ensemble forecasting system is run experimentally once a
month, based on the GLOBO model. GLOBO is an atmospheric general circulation model developed at
ISAC. The evolution of SST is represented by a simple slab ocean model based on surface flux balance
with a relaxation term to climatological SST. Recently, a new definition of the slab ocean model which
includes a flux correction term has been implemented to improve the SST simulation. It has been tested
in parallel with the operational forecast for some months of 2011. The results show that the globally
averaged root mean square forecast error of the SST simulated with the new model is slightly larger
than the operational one. However, the ensemble spread of the SST predicted with the new model
increases significantly and becomes very similar to the observed SST variability, in particular in the
Northern Hemisphere. The atmospheric field differences between the new and operational forecasts
show that SST has an impact in the second part of the month, especially in the Southern Hemisphere.
The ensemble spread of atmospheric parameters shows a slight increase using the new slab model.
However, its impact on the anomaly forecast fields is small.
4
Aider, Rabah. « Skill of monthly and seasonal forecasts using a Canadian general circulation model ». Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=32296.
Texte intégralRésumé :
An analysis of the co-variability of surface air temperature and precipitation over North America and Pacific SST is conducted using an SVD analysis. The leading SVD mode revealed a strong link between November SST anomalies and winter surface air temperature (SAT) and precipitation anomalies. In summer this relationship is much weaker. In winter GCM3 captured well the Pacific SST forcing and its response, particularly on the SAT pattern, but its response is less accurate in the summer. The monthly and seasonal forecasts using GCM3 are also analyzed. Precipitation forecasts showed little skill, especially in the warm season where the SST forcing is weak. Furthermore, GCM3 has low seasonal predictive skill in forecasting drought events over the Canadian prairies. However, the model has generally good predictive skill for 500 hPa heights and SAT, with higher scores in the winter. The skill is concentrated in the first month of the prediction period and decreases as the lead time is extended to one month.Une analyse de la co-variabilité entre la température de l'air au sol (SAT) ainsi que les précipitations en Amérique du Nord et la température de l'océan Pacifique à la surface (SST), a été faite en utilisant la méthode SVD. Le mode dominant de la SVD a révélé une relation forte entre les anomalies de la SST du mois de novembre et celles de la SAT et des précipitations hivernales. Ce lien est beaucoup plus faible en été. Le modèle GCM3 reproduit assez bien la réponse au forçage de la SST, particulièrement sur les patrons de la SAT, mais sa réponse est beaucoup moins précise en été. Les prévisions mensuelles et saisonnières de GCM3 ont aussi été analysées. Les capacités de GCM3 à prévoir les précipitations sont faibles, surtout en été où le forçage de la SST est aussi faible. De plus, le modèle ne possède pas d'habiletés notables à prédire les sécheresses dans les prairies Canadiennes. Par contre, les capacités prévisionnelles du modèle concernant la SAT et le géopotentiel à 500 hPa sont généralement assez élevées, particulièrement en hivers. Les habiletés de GCM3 sont concentrées dans le premier mois de la période de prévision, puis déclinent lorsque le délai d'émission est prolongé.
5
Kim, Young-Oh. « The value of monthly and seasonal forecasts in Bayesian stochastic dynamic programming / ». Thesis, Connect to this title online ; UW restricted, 1996. http://hdl.handle.net/1773/10142.
Texte intégral
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Maitaria, Kazungu. « ENABLING HYDROLOGICAL INTERPRETATION OF MONTHLY TO SEASONAL PRECIPITATION FORECASTS IN THE CORE NORTH AMERICAN MONSOON REGION ». Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/193926.
Texte intégralRésumé :
The aim of the research undertaken in this dissertation was to use medium-range to seasonal precipitation forecasts for hydrologic applications for catchments in the core North American Monsoon (NAM) region. To this end, it was necessary to develop a better understanding of the physical and statistical relationships between runoff processes and the temporal statistics of rainfall. To achieve this goal, development of statistically downscaled estimates of warm season precipitation over the core region of the North American Monsoon Experiment (NAME) were developed. Currently, NAM precipitation is poorly predicted on local and regional scales by Global Circulation Models (GCMs). The downscaling technique used here, the K-Nearest Neighbor (KNN) model, combines information from retrospective GCM forecasts with simultaneous historical observations to infer statistical relationships between the low-resolution GCM fields and the locally-observed precipitation records. The stochastic nature of monsoon rainfall presents significant challenges for downscaling efforts and, therefore, necessitate a regionalization and an ensemble or probabilistic-based approach to quantitative precipitation forecasting. It was found that regionalization of the precipitation climatology prior to downscaling using KNN offered significant advantages in terms of improved skill scores.Selected output variables from retrospective ensemble runs of the National Centers for Environmental Predictions medium-range forecast (MRF) model were fed into the KNN downscaling model. The quality of the downscaled precipitation forecasts was evaluated in terms of a standard suite of ensemble verification metrics. This study represents the first time the KNN model has been successfully applied within a warm season convective climate regime and shown to produce skillful and reliable ensemble forecasts of daily precipitation out to a lead time of four to six days, depending on the forecast month.Knowledge of the behavior of the regional hydrologic systems in NAM was transferred into a modeling framework aimed at improving intra-seasonal hydrologic predictions. To this end, a robust lumped-parameter computational model of intermediate conceptual complexity was calibrated and applied to generate streamflow in three unregulated test basins in the core region of the NAM. The modeled response to different time-accumulated KNN-generated precipitation forcing was investigated. Although the model had some difficulty in accurately simulating hydrologic fluxes on the basis of Hortonian runoff principles only, the preliminary results achieved from this study are encouraging. The primary and most novel finding from this study is an improved predictability of the NAM system using state-of-the-art ensemble forecasting systems. Additionally, this research significantly enhanced the utility of the MRF ensemble forecasts and made them reliable for regional hydrologic applications. Finally, monthly streamflow simulations (from an ensemble-based approach) have been demonstrated. Estimated ensemble forecasts provide quantitative estimates of uncertainty associated with our model forecasts.
7
Tennant, Warren James. « A monthly forecast strategy for Southern Africa ». Thesis, 1998. https://hdl.handle.net/10539/26794.
Texte intégralRésumé :
Dissertation submitted to the Faculty of Science,
University of the Witwatersrand, Johannesburg
for the Degree of Master of ScienceVarious techniques and procedures suited to monthly forecasting are investigated and tested. These include using the products generated by atmospheric general circulation models during a 17-year hindcast experiment, and downscaling the forecast circulation to regional rainfall in South Africa using circulation indices and canonical correlation analysis. The downscaling methods are evaluated using the cross-validation technique. Various model forecast bias-correction methods and skill-enhancing ensemble techniques are employed to improve the 30-day prognosis of the model. Forecasts from the general circulation model and each technique are evaluated. Those demonstrating reasonable skill over the southern Africa region, and which are feasible when considering available resources, are adopted into a strategy which can be used operationally to produce monthly outlooks. Various practical issues regarding the operational aspects of long-term forecasting are also discussed.
Andrew Chakane 2019
8
Pei-MinZhao et 趙培珉. « Dry Season Monthly Rainfall Forecast for Tseng-Wen Reservoir Catchment ». Thesis, 2015. http://ndltd.ncl.edu.tw/handle/17441733305543664412.
Texte intégral
9
Tseng, Pin-Han, et 曾品涵. « A development of statistic forecast system for pentad to monthly scales prediction ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/28490115029542569801.
Texte intégralRésumé :
碩士國立中央大學
大氣物理研究所
98
ABSTRACT The main purpose of this research is to develop a statistic forecast system for pentad to monthly scales prediction. The basic structure of this system was built by the persistence neutralization method and the linear regressive model. The persistence neutralization method filtered out the persistence of variables to distinguish the relationship between lead time and lag time. It had better performance than the persistence forecast. At first, the persistence neutralization method was used to transform the variables of predictand for neutralizing the persistence effect in climate data. Then, the predictive predictors were picked out by using the linear regressive model to develop a statistic forecast system for pentad to monthly scales prediction. 60 climate variables were used, including the outgoing longwave radiation (OLR), sea surface temperature (SST), estimated precipitation version1 (Precip), and mean sea level pressure (mslp), etc. Because each variable had different seasonal influence, the annual data were divided into six periods to construct the prediction system. First, we used the persistence neutralization method and the linear regressive model to neutralize and filter out of the persistence effect in 60 kinds of climate variables. The OLR field was used to be predictand and all 60 climate variables were used to be predictors. Each predictors had different predictive skill in different periods. We calculated the correlation coefficient and root mean square errors between OLR (predictand) and all climate variables. The spacial distribution of correlation coefficient between 40oS and 40oN was exhibited the relationship between predictand and predictors. 11 variables were selected in January and February. The correlation coefficient was more than 0.8 over the tropical Eastern Pacific and exceeded 0.6 in the north of Australia, Indonesia, Philippine, and South China Sea. In March and April, the correlation coefficient was more than 0.8 from the date line to 70oW on tropical Eastern Pacific and was about 0.6 near 120oE from 10oN to the Equator. In May and June, the correlation coefficient was 0.7 near 120oW on tropical Pacific Ocean, from 160oE to 70oW in Pacific Ocean, South America, and Australia. There was more than 0.8 in South Africa. High correlation exited from 0oE to 60oE and 40oN to 20oS in July and August. In September and October, the correlation coefficient was more than 0.7 from 120oE to 0oE and 40oN to 20oS and was 0.6 near 20oS in South America. The correlation coefficient in November and December were similar to September and October. But the atmos column precipitation water and absolute vorticity on 850hPa showed the best predictive skill to predict OLR. The high correlation areas between predictand and each predictor were dissimilar in different periods, but displayed consistency in same period.
10
Lai, Chia Liang, et 賴佳良. « Application of Soft Computing Techniques with Fourier Series to Forecast Monthly Electricity Demand ». Thesis, 2016. http://ndltd.ncl.edu.tw/handle/23171218166774438081.
Texte intégralRésumé :
碩士國立清華大學
工業工程與工程管理學系
104
The information from electricity demand forecasting helps energy generation enterprises develop an electricity supply system. This study aims to develop a monthly electricity forecasting model to predict the electricity demand for energy management. Given that the influence of weather factors, such as temperature and humidity, is diluted in the overall monthly electricity demand, the forecasting model uses historical electricity consumption data as an integrated factor to obtain future prediction. The proposed approach is applied to a monthly electricity demand time series forecasting model that includes trend and fluctuation series, of which the former describes the trend of the electricity demand series and the latter describes the periodic fluctuation imbedded in the trend. An integrated genetic algorithm and neural network model (GANN) is then trained to forecast the trend series. Given that the fluctuation series demonstrates an oscillatory behavior, we apply Fourier series to fit the fluctuation series. The complete demand model is named GANN–Fourier series. U.S. electricity demand data are used to evaluate the proposed model and to compare the results of applying this model with those of using conventional neural networks.
11
Tsai, Chang-He, et 蔡長河. « Using Macroeconomic Variables, Technical Indexes Variables, Foreign Stock Indexes Variables to Forecast the Monthly Return of TAIEX ». Thesis, 2008. http://ndltd.ncl.edu.tw/handle/45682909821296210029.
Texte intégralRésumé :
碩士國立臺北大學
企業管理學系
96
Taiwan’s security market has been established more than 40 years. In this period have been many times bullish market and many times bear market. Everyone who buys stock knows the only way to make money in the stock market is buys stock at low price and sells stock at high. But investors don’t know when to buy stock and when to sell. This research used Back Propagation Neural Network to forecast the return of TAIEX, and used monthly data from August 1988 to June 2006 of the TAIEX, macroeconomic variables, Technical Indexes variables, Foreign Stock Indexes variables, all variables. From the result of the research, we have several findings. First, using the all variables to forecast has the best directional and explainable effect in training period and best directional effect in testing period. Second, the result of the Mean Absolute Percentage Error (MAPE) and root mean squared error (RMSE) are different in training period.
12
Wang, Chao-Cheng, et 王朝正. « The Information Content and the Comparison of Earnings Forecast of Monthly Revenue of Parent and Consolidated Company ». Thesis, 2007. http://ndltd.ncl.edu.tw/handle/53106753986022989019.
Texte intégralRésumé :
碩士國立臺北大學
會計學系
95
According to past research, there is relevance between the earnings and the stock price. However, investors might make wrong decisions in their investment because of waiting for the earnings announcement. Therefore, this thesis will focus on monthly revenue of parent and consolidated company that the waiting is shorter to provide assistance for investors to make right decisions. Second, investors might think about everything that can affect stock price. Hence, this thesis will test whether the monthly revenue forecasting model is more precise than traditional one. The thesis will use regression model and T test to prove the relevance between monthly revenue of parent and consolidated company, and use MSE、MAPE and T test to test the difference between monthly and traditional forecasting model. The result reveals: (1) The relevance between monthly revenue of parent and consolidated company and stock price is significant positive, but the effect is no difference between monthly revenue of parent and consolidated company. (2) Forecasting model based on monthly revenue is more precise than traditional one, and monthly consolidated forecasting model is more accurate than monthly parent forecasting model.
13
Yeh, Tzu-Yu, et 葉芷妤. « Combining the Data of Google Trend to Forecast the Monthly Revenue of Firms in Taiwan\'s Telecom Industry ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/zdhvd8.
Texte intégralRésumé :
碩士元智大學
資訊管理學系
107
In this study, we applied the econometric models to evaluate the extension of revenues of three major telecom companies in Taiwan. By this way, we hope the study results can help them to improve their operational efficiency. Besides, the significant variables in the model can also provide the directions of improving competency for the telecom operators. Based on the existing theories and the reference of relevant literatures, the regression model was established, and the statistical verification, such as moving average autoregressive model (ARIMA), autoregressive model (AR), mean moving average autoregressive model (MA), vector autoregressive model (VAR), and single root test, were used to figure out the best fitted regression model. This study collect the revenue data of Taiwan Mobile Co., Ltd., Chunghwa Telecom Co., Ltd., Far EasTone Telecommunications Co., Ltd.’s data, and Google trend data (key words in Chinese and English, the stock code, Chinese and stock code) in the period of April 2004 to April 2018 to predict their revenues from May 2018 to April 2019. Our results found that, the usage of data through related keyword searching frequency in google trend, with the combination of the monthly data in revenues of the Taiwan’s top 3 telecom companies, the VAR model is best model with the highest accuracy of prediction on their monthly revenues. Furthermore, according to our results, the revenue of Taiwan Mobile Co.Ltd. and Far EasTone Telecommunications Co., Ltd. are more fluctuations, the revenue of Chunghwa Telecom Co. Ltd. is relatively stable in the future.
14
Chang, Rue-Xing, et 張瑞興. « Money Market Rate of Month Forecasts : Using Quarterly and Tenly Datas Help to Rate of Month Forecast ». Thesis, 1995. http://ndltd.ncl.edu.tw/handle/65758867076429979323.
Texte intégral
15
CHEN, CHIEN-TING, et 陳建廷. « Time Series Model Forecasts and Case Studies on Monthly Average Price of Agricultural Products ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7xue3m.
Texte intégralRésumé :
碩士國立高雄科技大學
金融資訊系
107
In recent years, we have often seen in the news reports that the price of fruits and vegetables has risen and fallen sharply. It greatly impacts on farmers' production and consumer prices. If there is a more accurate forecast of agricultural product prices, it could guide farmers' production decisions and stabilize price fluctuations. Based on the open data provided by the COA agricultural product price query system, this study used different time series models to predict the prices of different agricultural products, and compares them with the actual agricultural product prices to find the optimal forecast for different crops model. This results could guide farmers to make planting items and time decisions. After case study and analysis, it is found that each agricultural product had its own uniqueness. Furthermore, the fruits and vegetables would have a mutual substitution effect, especially in the summer when the fruit of Taiwan is rich in production. If it is caught in the hot weather, early harvesting would result in products price falling, and once it encountered a typhoon, it was another price trend. Therefore, there are not many measures that agricultural administrative units can adopt in monitoring the prices of agricultural products. The fundamental solution lies in the fact that farmers improve the quality of agricultural products, create their own brands and make market segments to decrease the impact of market price fall.
16
Liu, Jui Teng, et 劉睿騰. « The Study of Forecast Ability of High Turnover Mutual Fund Managers by Intra-Month Round-Trip Trading Data ». Thesis, 2009. http://ndltd.ncl.edu.tw/handle/07004203931504251583.
Texte intégralRésumé :
碩士實踐大學
企業管理學系碩士班
97
This paper presents the forecast ability of high turnover mutual fund managers by investigating returns of monthly round-trip trading data and tracking future stock price of the stock in the round-trip trading. After analyzing 8,312 monthly round-trip trading data of monthly high turnover mutual funds from May 2001 to February 2008, We find that the mean return of monthly round-trip transactions is significantly negative. Furthermore, the 1 to 6-month returns after round-trip transaction are significantly positive. These results show that high turnover mutual fund managers make a loss from their intra-month round-trip transactions, and they even lose potential profit opportunities of selling stock at higher price in the future. Therefore, we conclude that high turnover mutual fund managers are not good short-term institutional traders. Moreover, we suspect stock price forecast ability of high turnover mutual fund managers. Keywords: mutual fund, high turnover rate, transaction data, forecast ability
17
Tsao, Chia-Min, et 曹家敏. « A Research on the Operation Forecast of the Close Price before the Maturity Date of the Index Future Market Within This Month ». Thesis, 2004. http://ndltd.ncl.edu.tw/handle/15693689834003650787.
Texte intégralRésumé :
碩士國立高雄第一科技大學
風險管理與保險所
92
In recently years, the Foreign Capital Enterprises attempt to pull-up or pushdown the price on Spot Market on purpose before Maturity Date of Futures Contract in order to gain profits with their own shares on Index Futures Market. They try to affect the Final Settlement Price of Maturity Date, so that no matter the price of Futures Market and Spot Market is up-convergence or down-convergence, it exists the phenomenon of artificial manipulation. In this article, we took daily data of Futures Market and Spot Market as variables, and daily transaction data of Taiwan Futures Exchange Weighting stock price index of Futures Market and Spot Market between Sep. 3, 2003 and May 24, 2004 as research samples. It shows that: 1. The rate of return between Maturity Date of Index Futures Market and the day before due date, it always shows positive correlation on Futures net positions of Foreign capital. 2. The date of settlement day and due date are price difference expansion but after settlement, it will restrains on price different immediately.It means it is risky at that day, on the other hand, if it is correct on predicting; the return value would be huge. 3. The result of Co-Integration shows Taiwan futures index and spot index really exist co-integration. It means these two markets are on Long-term stable balanced relations. From error correction model, spot market revise downward the price but futures market is going up and the speed of correct on the future market is faster than spot market.