Metode Dekomposisi Multiplikatif Rata-rata Bergerak Untuk Peramalan Tingkat Produksi Padi Ladang Sulawesi Tengah

Field rice is a rice plants that is planted in a sedentary or shifting location. This study aims to forecast field rice production using the Multiplicative Decomposition method of moving average, and to determine the size of forecasting accuracy using Tracking signal, data used is the data from Central Sulawesi Province Field rice production in 2008-2016 obtained from the Agriculture Service of Central Sulawesi Province The research procedure is begun by analyzing the components of decomposition, namely the components of trend (T), seasonal (S), cyclic (C) and random components (I) then multiplies the value of these components. Forecasting results using the decomposition method helping by the Minitab 18 application in 2017 show that the pattern of the data contains a declining trend with the equation Yt = 1895.60 - 7.97 × t, and has a strong seasonal pattern with the expected pattern of data that increases or decreases in certain months such as March, April, August and December. The forecasting results obtained are at the control limit of Tracking signal which is between -4 to +4 that means the forecasting of rice production in the province of Central Sulawesi in 2017 using the moving average Multiplicative Decomposition method is valid

Generating Calibrated Ensembles of Physically Realistic, High-Resolution Precipitation Forecast Fields Based on GEFS Model Output

Enhancements of multivariate postprocessing approaches are presented that generate statistically calibrated ensembles of high-resolution precipitation forecast fields with physically realistic spatial and temporal structures based on precipitation forecasts from the Global Ensemble Forecast System (GEFS). Calibrated marginal distributions are obtained with a heteroscedastic regression approach using censored, shifted gamma distributions. To generate spatiotemporal forecast fields, a new variant of the recently proposed minimum divergence Schaake shuffle technique, which selects a set of historic dates in such a way that the associated analysis fields have marginal distributions that resemble the calibrated forecast distributions, is proposed. This variant performs univariate postprocessing at the forecast grid scale and disaggregates these coarse-scale precipitation amounts to the analysis grid by deriving a multiplicative adjustment function and using it to modify the historic analysis fields such that they match the calibrated coarse-scale precipitation forecasts. In addition, an extension of the ensemble copula coupling (ECC) technique is proposed. A mapping function is constructed that maps each raw ensemble forecast field to a high-resolution forecast field such that the resulting downscaled ensemble has the prescribed marginal distributions. A case study over an area that covers the Russian River watershed in California is presented, which shows that the forecast fields generated by the two new techniques have a physically realistic spatial structure. Quantitative verification shows that they also represent the distribution of subgrid-scale precipitation amounts better than the forecast fields generated by the standard Schaake shuffle or the ECC-Q reordering approaches.

A Displacement and Amplitude Score Employing an Optical Flow Technique

A field verification measure for precipitation forecasts is presented that combines distance and amplitude errors. It is based on an optical flow algorithm that defines a vector field that deforms, or morphs, one image to match another. When the forecast field is morphed to match the observation field, then for any point in the observation field, the magnitude of the displacement vector gives the distance to the corresponding forecast object (if any), while the difference between the observation and the morphed forecast is the amplitude error. Similarly, morphing the observation field onto the forecast field gives displacement and amplitude errors for forecast features. If observed and forecast features are separated by more than a prescribed maximum search distance, they are not matched to each other, but they are considered to be two separate amplitude errors: a missed event and a false alarm. The displacement and amplitude error components are combined to produce a displacement and amplitude score (DAS). The two components are weighted according to the principle that a displacement error equal to the maximum search distance is equivalent to the amplitude error that would be obtained by a forecast and an observed feature that are too far apart to be matched. The new score, DAS, is applied to the idealized and observed test cases of the Spatial Verification Methods Intercomparison Project (ICP) and is found to accurately measure displacement errors and quantify combined displacement and amplitude errors reasonably well, although with some limitations due to the inability of the image matcher to perfectly match complex fields.

Forecasting telecommunication new service demand by analogy method and combined forecast

In the modeling forecast field, we are usually faced with the more difficult problems of forecasting market demand for a new service or product. A new service or product is defined as that there is absence of historical data in this new market. We hardly use models to execute the forecasting work directly. In the Taiwan telecommunication industry, after liberalization in 1996, there are many new services opened continually. For optimal investment, it is necessary that the operators, who have been granted the concessions and licenses, forecast this new service within their planning process. Though there are some methods to solve or avoid this predicament, in this paper, we will propose one forecasting procedure that integrates the concept of analogy method and the idea of combined forecast to generate new service forecast. In view of the above, the first half of this paper describes the procedure of analogy method and the approach of combined forecast, and the second half provides the case of forecasting low-tier phone demand in Taiwan to illustrate this procedure's feasibility.