PERAMALAN KUNJUNGAN WISATAWAN KE PALEMBANG: PEMODELAN DATA TIME SERIES LINEAR VS NONLINEAR

Abstract — Forecasting is one of the statistical models play animportant role in decision making. Forecasting aims topredict / forecast what will happen in the future based on pastdata. One of the models used in forecasting is time seriesmodel. Forecasting techniques used in modeling of time seriesdata is a time series model linear and non-linear time series.Linear time series model include exponential smoothing, AutoRegressive Integrated Moving avarege (ARIMA) orBox-Jenkins and others. While non-linear time series modelsinclude Artifisal Neural Network (ANN), Fuzzy and others.The models are typically used to forecast financial data (suchas forecasting the exchange rate (exchange rate), forecastinggross domestic product, forecasting stock prices, and others).But in this study, both models will be used to forecast datatourist visits to Palembang of South Sumatra province, wherethe data of tourists visit marked by patterns of seasonalityand strong volatility, causing the data tend to benon-stationary and it becomes difficult to model. Forecastingis intended for South Sumatra tourism planning and to planfor infrastructure development needs. The secondperformance of each model for MSE and MAE is for thelinear model 0.6564 and 10:36, while for the nonlinear modelis 1.09E-22 and 7.31E-12. From this it appears that thenonlinear models are superior in predicting the number oftourists compared to the linear model.Keywords— Forecasting, linear and nonlinear time series,tourist visits . Abstrak— Forecasting adalah salah satu model statistikmemainkan peran penting dalam pengambilan keputusan.Peramalan bertujuan untuk memprediksi / perkiraan apayang akan terjadi di masa depan berdasarkan data masa lalu.Salah satu model yang digunakan dalam peramalan modeltime series. teknik yang digunakan dalam pemodelan datatime series Peramalan adalah model rangkaian waktu lineardan non-linear time series. waktu linier seri model termasukpemulusan eksponensial, Auto Regresif Integrated Movingavarege (ARIMA) atau Box-Jenkins dan lain-lain. Sementaranon-linear model time series termasuk Artifisal NeuralNetwork (ANN), Fuzzy dan lain-lain. Model biasanyadigunakan untuk meramalkan data keuangan (sepertiperamalan nilai tukar (kurs), peramalan produk domestikbruto, peramalan harga saham, dan lain-lain). Namun dalampenelitian ini, kedua model akan digunakan untuk meramalkan data kunjungan wisatawan ke PalembangProvinsi Sumatera Selatan, di mana data kunjunganwisatawan ditandai dengan pola musiman dan volatilitas yangkuat, menyebabkan data cenderung non-stasioner danmenjadi sulit untuk model. Peramalan ditujukan untukperencanaan pariwisata Sumatera Selatan dan merencanakanuntuk kebutuhan pembangunan infrastruktur. Kinerja keduamasing-masing model untuk MSE dan MAE adalah untukmodel linear 0,6564 dan 10:36, sedangkan untuk modelnonlinear adalah 1.09E-22 dan 7.31E-12. Dari sini tampakbahwa model nonlinear lebih unggul dalam memprediksijumlah wisatawan dibandingkan dengan model linear.Kata kunci— Peramalan, linear dan nonlinear time series,kunjungan wisatawan

Forecasting and Forecast Combination in Airline Revenue Management Applications

Predicting a variable for a future point in time helps planning for unknown future situations and is common practice in many areas such as economics, finance, manufacturing, weather and natural sciences. This paper investigates and compares approaches to forecasting and forecast combination that can be applied to service industry in general and to airline industry in particular. Furthermore, possibilities to include additionally available data like passenger-based information are discussed

Review of Nature-Inspired Forecast Combination Techniques

Effective and efficient planning in various areas can be significantly supported by forecasting a variable like an economy growth rate or product demand numbers for a future point in time. More than one forecast for the same variable is often available, leading to the question whether one should choose one of the single models or combine several of them to obtain a forecast with improved accuracy. In the almost 40 years of research in the area of forecast combination, an impressive amount of work has been done. This paper reviews forecast combination techniques that are nonlinear and have in some way been inspired by nature

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

European exchange trading funds trading with locally weighted support vector regression

In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series