Time Series Forecasting

Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in Oðn log nÞ time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.

The usual practices of air quality time-series forecasting are based on applying the models that deal with either the linear or nonlinear patterns. As the linear or nonlinear behavior of the time series is not known in advance, one applies the number of models and finally selects the one, which provides the most accurate results. The air pollutant concentration time series contain patterns that are not purely linear or nonlinear and applying either technique may give inadequate results. This study aims to develop a hybrid methodology that can deal with both the linear and nonlinear structure of the time series. The hybrid model is developed using the combination of autoregressive integrated moving average model, which deals with linear patterns and nonlinear dynamical model. To demonstrate the utility of the proposed technique, nitrogen dioxide concentration observed at a site in Delhi during 1999 to 2003 was utilized. The individual linear and nonlinear models were also applied in order to examine the performance of the hybrid model. The performance is compared for one-step and multi-step ahead forecasts using the error statistics such as mean absolute percentage error and relative error. It is observed that hybrid model outperforms the individual linear and nonlinear models. The exploitation of unique features of linear and nonlinear models makes it a powerful technique to predict the air pollutant concentrations.

The ability to forecast the future based on past data is a key tool to support individual and organizational decision making. In particular, the goal of Time Series Forecasting (TSF) is to predict the behavior of complex systems by looking only at past patterns of the same phenomenon. In recent years, several works in the literature have adopted Evolutionary Artificial Neural Networks (EANN) for TSF. In this work, we propose a novel EANN approach, where a weighted n-fold validation fitness scheme is used to build an ensemble of neural networks, under four different combination methods: mean, median, softmax and rank-based. Several experiments were held, using six real-world time series with different characteristics and from distinct domains. Overall, the proposed approach achieved competitive results when compared with a non-weighted n-fold EANN ensemble, the simpler 0-fold EANN and also the popular Holt-Winters statistical method.

In order to meet demand for family-based forecasting systems, firms such as IBM and American Software, Inc. offer software packages capable of forecasting demand for individual items and families of items. These systems, sometimes referred to as constrained or hierarchical forecasting (HF), are based upon a user defined strategy for aggregating items into families. HF systems are for determining forecasts for items and their respective families following either a direct strategy, which utilizes the contemporaneous demand values to compute a forecast, or a derived strategy, which determines forecasts for items by proportioning a parent family forecast. The objectives of HF systems include improved forecast performance and a reduction in the overall forecasting burden. HF strategies rely upon the premise that forecasts for families of items are more accurate than forecasts for individual items. Research literature indicates that HF system forecast performance is dependent upon the statistical nature of the items comprising the aggregated family. This literature further suggests that families be formed such that important information (demand patterns) exhibited within the item-level time series’ does not get lost when summing these series to define the aggregated group time series. Therefore, families of HF systems would logically be defined to provide a high degree of homogeneity, or positive correlation among family members. This strategy suggests that a group of homogenous items will tend to result in an aggregate time series which exhibits (or at least does not obscure) data patterns present in time series of the component items comprising the family. This study is an examination of strategies and guidelines for specifying aggregated families within HF systems. In particular, simulation is used as the vehicle to measure forecast performance at the aggregate (family) level. Varying degrees of systematically controlled, statistical correlation between subaggregate (item) time series comprising the forecast families is investigated using all four combinations of two forecast smoothing techniques and two forecast strategies. The results of this study support the research literature conclusion that higher positive correlation does indeed lead to improved forecast performance at the aggregate level. However, the results also indicate that higher negative correlation also leads to improved forecast performance at the aggregate level. An additional observation suggests that direct forecasts of an aggregate variable are more accurate than derived forecasts.Scope and purposeMany firms rely upon demand forecasts to conduct short-term planning activities such as purchasing, production planning and inventory control. These forecasts are often generated weekly. Firms that maintain a large product line consisting of thousands of items are faced with an especially burdensome forecast task. Consequently, firms such as IBM and American Software, Inc. offer software packages capable of forecasting demand for families of items. Family-based forecast systems attempt to improve forecast performance while offering a reduction in the overall forecasting burden. This study is an examination of strategies for specifying families within large forecast systems.

An artificial neural network (hence after, ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. In previous two decades, ANN applications in economics and finance; for such tasks as pattern reorganization, and time series forecasting, have dramatically increased. Many central banks use forecasting models based on ANN methodology for predicting various macroeconomic indicators, like inflation, GDP Growth and currency in circulation etc. In this paper, we have attempted to forecast monthly YoY inflation for Pakistan by using ANN for FY08 on the basis of monthly data of July 1993 to June 2007. We also compare the forecast performance of the ANN model with conventional univariate time series forecasting models such as AR(1) and ARIMA based models and observed that RMSE of ANN based forecasts is much less than the RMSE of forecasts based on AR(1) and ARIMA models. At least by this criterion forecast bas...

A framework (hereby named GA-SVM) for time series forecasting was formed by integration of the particular power of Genetic Algorithms (GAs) with the modeling power of the Support Vector Machine (SVM). The proposed system has potential to capture the benefits of both fascinating fields into a single framework. GAs offer high capability in choosing inputs that are relevant and necessary in predicting dependent variables. With these selected inputs, SVM becomes more accurate in modeling the estimation problems. Experiments demonstrated that the integrated GA-SVM approach is superior compared to conventional SVM applications.

Mercu Buana University Campus D is part of Mercu Buana University which began the operational in 2013. Since 2013 until 2017, Mercu Buana University Campus D still got less than a target about getting the new student.This can be due to various things including the lack of precisely marketing strategy undertaken.Therefore, in this study the authors make an application by implementing the concept of data mining using clustering and forecasting methods to obtain information from existing data registrants. So, the information can be used by decision makers to determine effective and efficient marketing strategies.

Consumer Price Index (CPI) is an important indicator used to determine inflation. The main objective of this research was to compare the forecasting ability of two time-series models using Zambia Monthly Consumer Price Index. We used monthly CPI data which were collected from January 2003 to December 2017. The models that were compared are the Autoregressive Integrated Moving average (ARIMA) model and Multicointegration (ECM) model. Results show that the ECM was the best fit model of CPI in Zambia since it showed smallest errors measures. Lastly, a forecast was done using the ECM and results show an average growth rate for food CPI at 6.63% and an average growth rate for nonfood CPI at 7.41%. Forecasting CPI is an important factor for any economy because it is essential in economic planning for the future. Hence, identifying a more accurate forecasting model is a major contribution to the development of Zambia.

In the last few years, the reduction of energy consumption and pollution became mandatory. It became also a common goal of many countries. Only in Europe, the building sector is responsible for the total 40% of energy consumption and 36% of CO2 pollution. Therefore, new control policies based on the forecast of buildings energy behaviors can be developed to reduce energy waste (i.e. policies for Demand Response and Demand Side Management). This paper discusses an innovative methodology for smart building indoor air-temperature forecasting. This methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of six years indoor air-temperature values of a building demonstrator. In detail, we have studied three characterizing rooms and the whole building. Experimental results of energy prediction are presented and discussed.

Forecasting is defined as an attempt to predict future events. It is a very important tool applied in a lot of sectors such as energy consumption, demand and supply, industrial and many more. An integration of a forecasting method with hybrid model, which become a combination of three methods, is believed to improve the forecasting performance. The method selected is the Exponential Smoothing method and ANFIS model.
The objectives of the research are to improve the forecasting accuracies of single model of exponential smoothing method, fuzzy logic model and Neural Network method and to develop the best model for forecasting data by integration of exponential smoothing model into ANFIS model.
Taylor (2003) proposed double seasonal Exponential Smoothing adapted from the Holt-Winters Exponential Smoothing formulation for short-term electricity demand forecasting. Kotsialos et al. (2005) addressed the problem of long-term sales forecasting using the Holt-Winters and Neural Network methods. Carvalho Jr., and Costa Jr., C. T. (2017) proposed a fuzzy forecasting methodology of tim series using a triangular membership function and applied the extended autocorrelation function. Singh, P. (2017) presented a model to forecast the Indian summer monsoon rainfall based on monthly and seasonal time scales using three techniques; fuzzy set, entropy and artificial neural network (ANN).
The research uses five approaches in forecasting data which are the exponential smoothing method, fuzzy logic model, Neural Network method, ANFIS hybrid model and the combination of exponential smoothing method with ANFIS model.
The research will describe the results from the five approaches models apply to the data. The best model for forecasting data will be developed and analysed using the forecasting accuracy measures.

The new model for forecasting will be develop from the research, which is from integrating the exponential smoothing method into the ANFIS model.

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.

ไฟฟ้าเป็นสินค้าประเภทหนึ่งที่มีลักษณะพิเศษคือ ไม่สามารถกักเก็บหรือรักษาสภาพสินค้าไว้ได้ ดังนั้น ปริมาณไฟฟ้าที่ถูกผลิตควรเท่ากับหรือใกล้เคียงกับปริมาณความต้องการใช้ไฟฟ้า ณ เวลานั้นๆ เนื่องจากถ้าหากผลิตมากเกินความต้องการ ก็จะทำให้เกิดการสูญเสียทรัพยากรที่ใช้ในการผลิตอย่างไร้ประโยชน์ แต่ถ้าหากมีการผลิตน้อยกว่าความต้องการ ก็จะทำให้เกิดเหตุการณ์ที่เรียกว่า Blackouts หรือไฟดับทั้งประเทศ ซึ่งจะสร้างความเสียหายต่อมูลค่าทางเศรษฐกิจที่เกิดจากกิจกรรมทางเศรษฐกิจต้องหยุดชะงักลง ดังนั้น การผลิตไฟฟ้าให้พอดีกับความต้องการใช้ไฟฟ้าจึงเป็นสิ่งสำคัญ

This paper examines stationary and nonstationary time series by formally testing for the presence of unit roots and seasonal unit roots prior to estimation, model selection and forecasting. Various Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models are estimated over the period 1975(1)-1989(4) for tourist arrivals to Australia from Hong Kong, Malaysia and Singapore. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as measures of forecast accuracy. As the best fitting ARIMA model is found to have the lowest RMSE, it is used to obtain post-sample forecasts. Tourist arrivals data for 1990(1) to 1996(4) are compared with the forecast performance of the ARIMA model for each origin market. The fitted ARIMA model forecasts tourist arrivals from Singapore between 1990(1)-1996(4) very well.

In wastewater industry, real-time sensing of surface temperature variations on concrete sewer pipes is paramount in assessing the rate of microbial-induced corrosion. However, the sensing systems are prone to failures due to the aggressively corrosive environmental conditions inside sewer assets. Therefore, reliable sensing in such infrastructures is vital for water utilities to enact efficient wastewater management. In this context, this paper presents a sensor failure detection and faulty data accommodation (SFDFDA) approach that aids to digitally monitor the health conditions of the sewer monitoring sensors. The SFDFDA approach embraces seasonal autoregressive integrated moving average (SARIMA) model with a statistical hypothesis testing technique for enabling temporal forecasting of sensor variable. Then, it identifies and isolates anomalies in a continuous stream of sensor data whilst detecting early sensor failure. Finally, the SFDFDA approach provides reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems. The SFDFDA approach was evaluated by using the surface temperature data sourced from the instrumented wastewater infrastructure and the results have demonstrated the effectiveness of the SFDFDA approach and its applicability to surface temperature monitoring sensor suites.

In recent years, artificial neural networks have been used for time series forecasting. Determining architecture of artificial neural networks is very important problem in the applications. In this study, the problem in which time series are forecasted by feed forward neural networks is examined. Various model selection criteria have been used for the determining architecture. In addition, a new model selection strategy based on well-known model selection criteria is proposed. Proposed strategy is applied to real and simulated time series. Moreover, a new direction accuracy criterion called modified direction accuracy criterion is discussed. The new model selection strategy is more reliable than known model selection criteria.

The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic's second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box-Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020-October 13, 2020 were extracted from the website of the Italian Ministry of Health (www. salute. gov. it). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities' decision-making, especially in the short-term.

This paper [1] uses the Geometric Brownian Motion (GBM) to model the behaviour of crude oil price in a Monte Carlo simulation framework. The performance of the GBM method is compared with the naive strategy using different forecast evaluation techniques. The results from the forecasting accuracy statistics suggest that the GBM outperforms the naive model and can act as a proxy for modelling movement of oil prices. We also test the empirical viability of using a call option contract to hedge oil price declines. The results from the simulations reveal that the single-step binomial price model can be effective in hedging oil price volatility. The findings from this paper will be of interest to the government of Nigeria that views the price of oil as one of the key variables in the national budget. JEL Classification Numbers : E64; C22; Q30 Keywords : Oil price volatility; Geometric Brownian Motion; Monte Carlo Simulation; Single-Step Binomial Price Model [1] Acknowledgement: We wish to...

The modeling of dengue fever cases is an important task to help public health officers to plan and prepare their resources to prevent dengue fever outbreak. In this paper, we present the time-series modeling of accumulated dengue fever cases acquired from the Malaysian Open Data Government Portal. Evaluation of the forecast for future dengue fever outbreak shows promising results, as evidence is presented for the trend and seasonal nature of dengue fever outbreaks in Malaysia.

A univariate time-series analysis method has been used to model and forecast the monthly number of dengue haemorrhagic fever (DHF) cases in southern Thailand. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1994-2005 and then validated the models using the data collected between January-August 2006. The results showed that the regressive forecast curves were consistent

Artificial neural network is a valuable tool for time series forecasting. In the case of performing multiperiodic forecasting with artificial neural networks, two methods, namely iterative and direct, can be used. In iterative method, first subsequent period information is predicted through past observations. Afterwards, the estimated value is used as an input; thereby the next period is predicted. The process is carried on until the end of the forecast horizon. In the direct forecast method, successive periods can be predicted all at once. Hence, this method is thought to yield better results as only observed data is utilized in order to predict future periods. In this study, forecasting was performed using direct and iterative methods, and results of the methods are compared using grey relational analysis to find the method which gives a better result.

There exists a wide range of paradigms, and a high number of different methodologies that are applied to the problem of time series prediction. Most of them are presented as a modified function approximation problem using input/output data, in which the input data are expanded using values of the series at previous steps. Thus, the model obtained normally predicts the value of the series at a time ðt þ hÞ using previous time steps ðt À t 1 Þ; ðt À t 2 Þ; . . . ; ðt À t n Þ. Nevertheless, learning a model for long term time series prediction might be seen as a more complicated task, since it might use its own outputs as inputs for long term prediction (recursive prediction). This paper presents the utility of two different methodologies, the TaSe fuzzy TSK model and the least-squares SVMs, to solve the problem of long term time series prediction using recursive prediction. This work also introduces some techniques that upgrade the performance of those advanced one-step-ahead models (and in general of any one-step-ahead model), where they are used recursively for long term time series prediction. r

Meta-Learning, the ability of learning to learn, helps to train a model to learn very quickly on a variety of learning tasks; adapting to any new environment with a minimal number of examples allows us to speed up the performance and training of the model. It solves the traditional machine learning paradigm problem, where it needed a vast dataset to learn any task to train the model from scratch. Much work has already been done on meta-learning in various learning environments, including reinforcement learning, regression task, classification task with image, and other datasets, but it is yet to be explored with the time-series domain. In this work, we aimed to understand the effectiveness of meta-learning algorithms in time series classification task with multivariate time-series datasets. We present the algorithm's performance on the time series archive, where the result shows that using meta-learning algorithms leads to faster convergence with fewer iteration over the non-meta-learning equivalent.

SARS-Cov-2 is a novel coronavirus strain that has not previously been associated with human infection. COVID-19 is the name given to the disease caused by SARS-Cov-2. The World Health Organization declared it a Public Health Emergency of International Concern on January 30, 2020, and was later declared a pandemic on March 11, 2020. In this paper, we used MAD, MAPE, and RMSE to measure the predictive accuracy of the forecasting models for the COVID-19 cases of the provinces/cities in the region, namely, Biliran, Eastern Samar, Northern Samar, Samar, Leyte, Southern Leyte, Tacloban City, and Ormoc City. The data for this study comes from an official COVID-19 cases dataset provided by the Regional Epidemiology & Surveillance Unit of Department of Health (DOH) – Eastern Visayas Center for Health and Development from March 1, 2020 to October 30, 2021. The dataset contains confirmed cases on a weekly basis for 87 weeks (79 weeks were used for forecasting and 8 weeks were used to determine the predictive accuracy). From the results, it has been found out that ARIMA models can be highly reliable in forecasting the cases in Eastern Visayas.

This study analyzes forecasts of Bitcoin price using the autoregressive integrated moving average (ARIMA) and neural network autoregression (NNAR) models. Employing the static forecast approach, we forecast next-day Bitcoin price both with and without re-estimation of the forecast model for each step. For cross-validation of forecast results, we consider two different training and test samples. In the first training-sample, NNAR performs better than ARIMA, while ARIMA outperforms NNAR in the second training-sample. Additionally, ARIMA with model re-estimation at each step outperforms NNAR in the two test-sample forecast periods. The Diebold Mariano test confirms the superiority of forecast results of ARIMA model over NNAR in the test-sample periods. Forecast performance of ARIMA models with and without re-estimation are identical for the estimated test-sample periods. Despite the sophistication of NNAR, this paper demonstrates ARIMA enduring power of volatile Bitcoin price prediction.

Abstract This paper analyzes the factor zoo, which has theoretical and empirical implications for finance, from a machine learning perspective. More specifically, we discuss feature selection in the context of deep neural network models to predict the stock price direction. We investigated a set of 124 technical analysis indicators used as explanatory variables in the recent literature and specialized trading websites. We applied three feature selection methods to shrink the feature set aiming to eliminate redundant information from similar indicators. Using daily data from stocks of seven global market indexes between 2008 and 2019, we tested neural networks with different settings of hidden layers and dropout rates. We compared various classification metrics, taking into account profitability and transaction costs levels to analyze economic gains. The results show that the variables were not uniformly chosen by the feature selection algorithms and that the out-of-sample accuracy rate of the prediction converged to two values — besides the 50% accuracy value that would suggest market efficiency, a “strange attractor” of 65% accuracy also was achieved consistently. We also found that the profitability of the strategies did not manage to significantly outperform the Buy-and-Hold strategy, even showing fairly large negative values for some hyperparameter combinations.

Запропоновано методику передпрогнозного фрактального аналізу часових рядів, яка базується на послідовному R/S-аналізі. На основі цієї методики можна визначати рівень персистентності, розраховувати середню величину неперіодичного циклу часових рядів, а також встановлювати інвестиційну якість (дохідність) активів, які представляються фінансовими часовими рядами. В рамках цієї методики запропоновано критерій визначення середньої довжини періодичного і неперіодичного циклів, на основі згладжування V-статистики за допомогою звичайних плинних середніх та адаптивної плинної Кауфмана.

Ключові слова: послідовний R/S-аналіз, показник Херста, часовий ряд, середня довжина неперіодичного циклу, інвестиційна якість фінансових активів

Предложена методика предпрогнозного фрактального анализа временных рядов, основанная на последовательном R/S-анализе. На основе этой методики можно определять уровень персистентности, рассчитывать среднюю длину непериодического цикла временных рядов, а также определять инвестиционное качество (доходность) активов, которые задаются финансовыми временными рядами. В рамках этой методики предложен критерий определения средней длины периодического и непериодического циклов на основе сглаживания V-статистики с помощью обычных скользящих средних и адаптивной средней Кауфмана.

Ключевые слова: последовательный R/S-анализ, показатель Херста, временной ряд, средняя длина непериодического цикла, инвестиционное качество финансовых активов.

The method of pre-forecasting fractal time series analysis, based on the sequential R/S-analysis is offered. Based on this technique it is possible to determine to the level of persistence, to calculate the average length of non-time series periodic cycle, and to establish investment quality (yield) of assets which are represented by financial time series. Under this method the criterion for the average length of periodic and non-periodic cycles determining, based on smoothing the V-statistics using simple moving average and Kaufman adaptive moving average is proposed.

Keywords: sequential R/S-analysis, Hurst index, time series, average length of non-time series periodic cycle, investment quality of assets

This research aims to evaluate two econometric models to forecast imports and exports for the financial year (FY) 2020. For this purpose, we used the annual exports and imports data of Pakistan from FY2002 to FY2019. Thus, in this regard, we employed, and compared the results of two econometrics models such as Box Jenkins or Autoregressive Integrated Moving Average (ARIMA), and Auto-Regressive (AR) with seasonal dummies. For examining the precision of forecasting, we employed mean absolute error and root mean square error approaches. The findings of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) reveal that the ARIMA or Box Jenkins approach provides better accuracy of the forecast for the exports as compared to the AR model with dummies. However, Auto-Regressive (AR) model has demonstrated more precision for the imports as compared to the Box Jenkins model. Hence, the projected forecasting for the growth of export is 1.87 % for the FY2020 and projected forecasting for t...

Penyusunan trend kunjungan wisatawan ini secara umum bertujuan untuk mengetahui gambaran pola kunjungan wisatawan ke destinasi di Provinsi Banten melalui pengolahan dan analisis data sekunder untuk;
• Mengetahui gambaran jumlah kunjungan wisatawan baik domestik maupun mancanegara ke destinasi wisata Provinsi Banten,
• Mengetahui tren kunjungan wisatawan ke ke objek-objek wisata di Provinsi Banten,  melalui analisis data statistik kepariwisataan yang ada,
• Menyusun usulan rekomendasi profiling, pendataan dan penyusunan serta penyajian informasi pariwisata berdasarkan konsep global,
• Terkumpulnya data cross-sectional (at a point of time) sabagai pelengkap data berkala yang digunakan untuk penyusunan analisis tren.
Adapun sasaran dari hasil analisis tren ini adalah para pemangku kepentingan di bidang pariwisata antara lain:
• Pemerintah Pusat, lembaga vertikal, Pemerintah Kabupaten/¬Kota, SKPD Provinsi Banten yang terkait dengan Instruksi Gubernur No. 1 Tahun 2006 tentang Pembangunan Kebudayaan dan Pariwisata;
• Kelembagaan Non Government Organization (NGO) di bidang Kebudayaan dan Pariwisata antara lain PHRI, ASITA, HPI;
• Masyarakat di lingkungan Destinasi Provinsi Banten;
• Masyarakat di lingkungan Akademisi Provinsi Banten;
• Serta pelaku usaha jasa bidang Budpar lainnya;

Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for forecasting of financial data series. The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. Neural Networks have the advantage that can approximate nonlinear functions. In this paper we compared the performances of different feed forward and recurrent neural networks and training algorithms for predicting the exchange rate EUR/RON and USD/RON. We used data series with daily exchange rates starting from 2005 until 2013.

Applying quantitative models for forecasting and assisting investment decision making has become more indispensable in business practices than ever before. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In the literature, several hybrid techniques have been proposed by combining different time series models together, in order to overcome the deficiencies of single models and yield hybrid models that are more accurate. In this paper, in contrast of the traditional hybrid models, a new methodology is proposed in order to construct a new class of hybrid models using a time series model as basis model and a classifier. As classifiers cannot be lonely applied as forecasting model for continuous problems, in the first stage of the proposed model, a forecasting model is used as basis model. Then, the estimated values of the basis model are modified in the second stage, based on the distinguished trend of the residuals of the basis model and the optimum step length, which are respectively calculated by a classifier model and a mathematical programming model. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way in order to construct a more accurate hybrid model than its basis time series model. Therefore, it can be used as an appropriate alternative model for forecasting tasks, especially when higher forecasting accuracy is needed.

This paper proposes a quantum learning scheme approach for time series forecasting, through the application of the new non-standard Qubit Neural Network (QNN) model. The QNN description was adapted in this work in order to resemble classical Artificial Neural Networks (ANNs). Three stock market series were predicted. The results are discussed over several statistics and are compared with ANNs experiments with equivalent degrees of freedom.

One of the major constraints on the use of backpropagation neural networks as a practical forecasting tool is the number of training patterns needed. We propose a methodology that reduces the data requirements. The general idea is to use the Box-Jenkins model in an exploratory phase to identify the 'lag components' of the series, to determine a compact network structure with one input unit for each lag, and then apply the validation procedure. This process minimizes the size of the network and consequently the data required to train the network. The results obtained in eight studies show the potential of the new methodology as an alternative to the traditional time-series models.

The data mining its main process is to collect, extract and store the valuable information and now-a-days it's done by many enterprises actively. In advanced analytics, Predictive analytics is the one of the branch which is mainly used to make predictions about future events which are unknown. Predictive analytics which uses various techniques from machine learning, statistics, data mining, modeling, and artificial intelligence for analyzing the current data and to make predictions about future. The two main objectives of predictive analytics are Regression and Classification. It is composed of various analytical and statistical techniques used for developing models which predicts the future occurrence, probabilities or events. Predictive analytics deals with both continuous changes and discontinuous changes. It provides a predictive score for each individual (healthcare patient, product SKU, customer, component, machine, or other organizational unit, etc.) to determine, or influence the organizational processes which pertain across huge numbers of individuals, like in fraud detection, manufacturing, credit risk assessment, marketing, and government operations including law enforcement.

In this work we present a large scale comparison study for the major machine learning models for time series forecasting. Specifically, we apply the models on the monthly M3 time series competition data (around a thousand time series). There have been very few, if any, large scale comparison studies for machine learning models for the regression or the time series forecasting problems, so we hope this study would fill this gap. The models considered are multilayer perceptron, Bayesian neural networks, radial basis functions, generalized regression neural networks (also called kernel regression), K-nearest neighbor regression, CART regression trees, support vector regression, and Gaussian processes. The study reveals significant differences between the different methods. The best two methods turned out to be the multilayer perceptron and the Gaussian process regression. In addition to model comparisons, we have tested different preprocessing methods and have shown that they have different impacts on the performance.

In this paper, we describe a series of simulations that serve as a verification of the abstract similarity between vehicular and animal navigation. Valentino Braitenberg used this similarity to illustrate that vehicles controlled by very simple biologically inspired circuits, manifest a wonderful diversity of complex animal behaviors. By constructing a series of experiments that are designed around the possibility of interchanging phenomena that affect vehicles or animals, we hope to show that this analogical similarity is a useful tool.

Resumen En este trabajo se presentan algoritmos basados en redes neuronales artificiales (RNA) para la predicción de series temporales de las variables oceanográficas Índice de Oscilación del Sur (IOS) y anomalías de temperatura superficial del mar (ATSM). Se realizaron experimentos de predicción utilizando la arquitectura de redes neuronales conocida como redes con respuesta finita al impulso (RNRFI). Se hicieron experimentos variando los diferentes parámetros de la RNRFI, para determinar aquéllos que permitieran un mejor comportamiento. Se encontró que para series temporales del IOS y las ATSMs los mejores resultados se presentan al conformar una RNRFI con una neurona de entrada, filtros de orden 10 en la capa de entrada, dos capas posteriores de 8 neuronas con filtros de orden 5 para cada una de ellas, y una neurona de salida. Todas las redes fueron entrenadas con el algoritmo de aprendizaje retropropagación temporal, usando la sigmoide como función de activación en las capas ocultas y salida lineal. La razón de aprendizaje usada fue de 0.001.

This study analyses the relationship between oil prices and the consumer price indices in Kenya. This is achieved by creating a statistical model to determine the impact of oil price fluctuations on the consumer price index. Oil is a vital commodity which is widely used as a source of energy in Kenya and around the world. It is used in industries to run machines, in homes for cooking and lighting and in transportation to power the different transport means. The Kenyan economy underwent major structural reforms in the early 1990s with a view to improving the overall macro-economic efficiency, increase incomes, create employment opportunities and improve the performance and productivity of public investments. These reforms included abolition of price controls allowing the market forces of demand and supply to determine prices. In line with these public sector reforms, the Kenya Government deregulated the downstream Petroleum Market operations on October 27th, 1994. However, it has been observed that the post deregulation retail prices of petroleum products have not closely followed the changes in international oil prices. When the international crude oil prices were rising in 2007 and 2008, oil marketers quickly passed on these increased costs to consumers, but took inordinately long to pass on cost reduction benefits to consumers when international oil prices were on a downward spiral in the last quarter of 2008. The purchasing power of many Kenyans is significantly reduced by price increases and they end up living low quality lives. The fluctuation of oil prices prompted the government to form the Energy Regulatory Commission (ERC), now known as Energy and Petroleum Regulatory Authority (EPRA), to set prices of gas and oil products based on prevailing international rates. This analysis provides scientific guidance and insights to governments and the oil industry players on the economic and social impact of oil price fluctuations on the livelihoods of ordinary citizens. It also provides some recommendations to address the problem.

Keywords: Consumer price index, Oil prices, Fluctuations, Energy Regulatory Commission

Aims: The aim of this work is to develop suitable ARIMA models which can be sued to forecast daily confirmed/death cases of COVID-19 in Nigeria. This is subject to developing the model, checking them for suitability and carrying out eight months forecast, and making recommendations for the Nigerian Health sector.