1,749,823 research outputs found
The decomposition of forecast in seasonal arima models.
Get PDFThis paper presents a procedure to break down the forecast function of a seasonal ARIMA model in terms of its permanent and transitory components. Both depend on the initial values at the forecast origin, but their structures are fixed and independent of this origin. The permanent component is an estimate of the long-run projection of the corresponding economic variable and the transitory element describes the approach towards the permanent one. Within the permanent component a distinction is made between the factors that depend on the initial conditions of the system and those that are deterministic. The procedure is compared to other methods presented in the literature and illustrated in an example.Forecast function; Long-term growth; Seasonal components; Trends; Unit roots;
IDENTIFIKASI MODEL FLUKTUASI INDEKS K HARIAN MENGGUNAKAN MODEL ARIMA (2.0.1)
No full textThe geomagnetic level called geomagnetic index. Based on the latitude, geomagnetic index for high to intermediate latitude is Kp index and for equator area is Dst index. For a certain location it is called local geomagnetic index, K index. Flunctuation of geomagnetic index is one of information that describes condition of space weather. Based on the above condition, this paper discusses identification of daily K index flunctuation model using Auto Regression Integrated Moving Average-ARIMA (2.0.1), 2, 0, and 1 order. Using observation data 1 order. Using observation data and reconstruction of four days data, it is found ARIMA (2.0.1) model 2, 0, and 1 order. Both model are validated, calculating errors and pattern correlation. Model validation result using observational data, showed the error of 2.18 and the pattern correlation of 0,99940. Model validation using reconstruction data, showed the error of 0.3582 and the pattern corelation of 0.9988.Tingkat gangguan geomagnet (medan magnet bumi) disebut indeks geomagnet. Apabila indeks geomagnet ditinjau berdasarkan lintang maka indeks geomagnet dari lintang tinggi hingga menengah adalah indeks Kp dan untuk daerah ekuator indeks Dst. Pada lokasi tertentu dinyatakan dengan indeks K geomagnet lokal. Fluktuasi indeks geomagnet merupakan salah satu informasi yang menunjukkan kondisi cuaca antariksa. Berdasarkan kondisi itu pada makalah ini dibahas identifikasi model fluktuasi indeks K harian menggunakan model Auto Regression Integrated Moving Average -ARIMA (2,0,1) orde 2, 0, dan 1. Menggunakan data pengamatan dan data rekonstruksi dari rata-rata empat harian, diperoleh model ARIMA (2,0,1), orde 2, 0, dan 1. Kedua model yang diperoleh tersebut divalidasi dengan menghitung galat dan korelasi pola. Hasil validasi model dengan data pengamatan mempunyai galat 2.18 dengan korelasi pola 0.99940. Validasi model dengan data rekonstruksi mempunyai galat 0.3582 dengan korelasi pola 0.9988.Hal. 100-11
An improved wavelet-ARIMA approach for forecasting metal prices
Get PDFMetal price forecasts support estimates of future profits from metal exploration and mining and inform purchasing, selling and other day-to-day activities in the metals industry. Past research has shown that cyclical behaviour is a dominant characteristic of metal prices. Wavelet analysis enables to capture this cyclicality by decomposing a time series into its frequency and time domain. This study assesses the usefulness of an improved combined wavelet-autoregressive integrated moving average (ARIMA) approach for forecasting monthly prices of aluminium, copper, lead and zinc. The performance of ARIMA models in forecasting metal prices is demonstrated to be increased substantially through a wavelet- based multiresolution analysis (MRA) prior to ARIMA model fitting. The approach demonstrated in this paper is novel because it identifies the optimal combination of the wavelet transform type, wavelet function and the number of decomposition levels used in the MRA and thereby increases the forecast accuracy significantly. The results showed that, on average, the proposed framework has the potential to increase the accuracy of one month ahead forecasts by 126/t for copper, 51/t for zinc, relative to classic ARIMA models. This highlights the importance of taking into account cyclicality when forecasting metal prices
Analysis of the Christian Town of Arima in 16th and 17th century
Get PDFFrom the 16th to the 17th century, the reports of Society of Jesus missionaries who visited Japan fill the blanks of Japanese history in the Middle Ages. However, for lack of Japanese documents, it is difficult to create a concrete local image technically. Arima was a central place in the Christian era and provides a representative example. I analyzed the local constitution of the Christian town of Arima, introduce the study results because I think they help to clarify the overall perspective.departmental bulletin pape
REGCMPNT : A Fortran Program for Regression Models with ARIMA Component Errors
Get PDFRegComponent models are time series models with linear regression mean functions and error terms that follow ARIMA (autoregressive-integrated-moving average) component time series models. Bell (2004) discusses these models and gives some underlying theoretical and computational results. The REGCMPNT program is a Fortran program for performing Gaussian maximum likelihood estimation, signal extraction, and forecasting with RegComponent models. In this paper we briefly examine the nature of RegComponent models, provide an overview of the REGCMPNT program, and then use three examples to show some important features of the program and to illustrate its application to various different RegComponent models.
Forecasting Financial Valuation Comparing ARIMA and Prophet
Get PDFIn this study, we limit the forecasting period to one year since the same method is applicable to more than one year. The reason for that is the only five-year historic data we have for Discount Cash Flow (DCF) and six years for Price Earning (P/E) and Price to Sale (P/S) valuation. We use a time series approach for comparison, so we use the last given year, which is 2018, for comparison with the predicted value based on all previous years. So, in this study, we compare the predicted 2018 year Using ARIMA and Prophet Methods with the real 2018 year. Some steps were taken to make forecasting and rebuild time series and apply forecasting methodologies. The errors between real data and the forecasted data for both methods shown almost similar results
Greater Arima local area plan.
No full textDraft document describing the proposed development plan for Greater Arima, including land use, population and housing, employment and economic activity, infrastructure, transportation, development concerns and proposals
Perspectiva histórica de los modelos ARIMA y su utilidad en el análisis económico
Get PDFEste trabajo comienza enumerando las contribuciones a la teoría de procesos estocásticos estacionarios que aparecieron entre 1912 y 1942 y comentando, al mismo tiempo,
los principales procedimientos existentes en las décadas de los cincuenta y sesenta
para predecir y extraer señales de series económicas que, en general, se consideraba que
no eran estacionarias. Esta brecha existente entre la aplicación práctica y el análisis teórico
fue cubierta por Box y Jenkins (1970) con los modelos ARJMA. Estos modelos
incorporan un tipo específico de procesos evolutivos que se caracteriza por la presencia
de raíces autorregresivas unitarias sobre una estructura ARIMA, la cual constituye una
forma general de aproximar procesos estacionarios.
En la literatura econométrica, la naturaleza evolutiva de las variables económicas se
capta también, generalmente, mediante el uso de raíces unitarias en los modelos econométricos
y/o en los modelos que generan las variables exógenas. Por ello, tal como se
ha ido discutiendo por diferentes autores, los modelos ARIMA no son cajas negras,
sino formas finales de modelos econométricos. Por tanto, los modelos ARIMA se pueden
utilizar de forma consistente, pero ineficiente, para describir y estimar el comportamiento
a largo y corto plazo de las series económicas, y tales resultados pueden ser
muy útiles en la elaboración de los informes que las instituciones privadas y públicas
realizan periódicamente.This paper starts enumerating some of the contributions to the theory of Stationary Stochastic Processes during the first half of this century and commenting the main procedures used in the fifties and sixties for forecasting and signal extraction based on the fact that a great number o( the observed economic time series are nonstationary. The gap between the theory and practice was filled by Box and Jenkins (1970) with the ARJMA models, which incorporate a specific type of processes characterized by the presence of unit autoregressive roots on top of a general approximation — ARMA models— for a linear stationary process. In the econometric literature the evolving nature of the economic variables is also generally captured by the same procedure of including unit roots into the model and for in the processes generating the exogenous variables, Therefore, as it has been widely discussed in the literature, the ARIMA models are not black-boxes but a sort of final forms of the econometric models. Thus, the ARIMA schemes can be employed as a consistent, but certainly inefficient, way of characterizing and estimating the long and short-term behaviour of economic time series, and these results can be used in the economic reports that private and public institutions need to procedure periodically.Publicad
Monatomic gas as a singular limit of relativistic theory of 15 moments with non-linear contribution of microscopic energy of molecular internal mode
No full textRecently a new relativistic model of polyatomic gases has been proposed, by Arima-CarrisiPennisi-Ruggeri (2022), in the context of Rational Extended Thermodynamics. It is based on a hierarchy of 15 moments of the Boltzmann-Chernikov equation that appropriately takes into account the non-linear contribution of the microscopic total energy of the molecule (the sum of the rest energy and of the energy of the molecular internal modes). In this paper, in the singular limit, under initial conditions compatible with monatomic gases, we prove that the 15-moments model for polyatomic gases leads, to the well-known 14-moments model of monatomic gases
A new mixed multiplicative-additive model for seasonal adjusment
Get PDFUsually, seasonal adjustment is based on time series models which decompose an unadjusted series into the sum or the product of four unobservable components (trendcycle, seasonal, working-day and irregular components). In the case of clearly weatherdependent output in the west German construction industry, traditional considerations lead to an additive model. However, this results in an over-adjustment of calendar effects. An alternative is a multiplicative-additive mixed model, the estimation of which is illustrated using X-12-ARIMA. Finally, the relevance of the new model is shown by analysing selected time series for different countries. --Seasonal adjustment,calendar adjustment,over-adjustment,multiplicative-additive model,X-12-ARIMA
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