Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term flow needs to consider many factors, the results of existing models for subway forecasting are often unsatisfactory. Along this line, we propose parallel architecture, called seasonal nonli...

Wind is a dominant source of renewable energy with high sustainability potential. However, the intermittence and unstable nature wind affect efficiency reliability conversion systems. The prediction available potential also heavily flawed by its nature. Thus, evaluating trough speed prevision, crucial for adapting production to load shifting user demand rates. This work aims forecast using stat...

Dynamic link resizing is an attractive approach for resource management in virtual private networks (VPNs) serving modern real-time and multimedia traffic. In this paper, we assess the use of linear traffic predictors to dynamically resize the bandwidth of VPN links. We present the results of performance comparisons of three predictors: Gaussian, autoregressive moving average (ARMA) and fractio...

Dataset with Outliers causes poor accuracy in future analysis of data mining tasks. To improve the performance of mining task, it is necessary to detect and revamp of outliers which are there in the dataset. Existing techniques like ARMA (Auto-Regressive Moving Average), ARIMA (AutoRegressive Integrated Moving Average) and Multivariate Linear Gaussian state space model don't consider the p...

Carbon dioxide can change the heat balance of atmosphere. To study relationship between CO 2 and temperature change, we use given concentrations to implement linear regression model, gray time series forecasting back-propagation auto-regressive moving average model establish growth function concentration. Errors are evaluated choose most suitable model. then in step one further predict future l...

In this paper, for the first time, the subject of change point estimation has been utilized in the stationary state of auto regressive moving average (ARMA) (1, 1). In the monitoring phase, in case the features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the maximum likelihood technique, an approach will be developed for the estimation of the stationary state’s change...

We study the autocorrelation structure and the spectral density function of aggregates from a discrete-time process. The underlying discrete-time process is assumed to be a stationary AutoRegressive Fractionally Integrated Moving-Average (ARFIMA) process, after suitable number of differencing if necessary. We derive closed-form expressions for the limiting autocorrelation function and the norma...

Traffic congestion research is on the rise, thanks to urbanization, economic growth, and industrialization. Developed countries invest a lot of money in collecting traffic data using Radio Frequency Identification (RFID), loop detectors, speed sensors, high-end light, GPS. However, these processes are expensive, infeasible, non-scalable for developing with numerous non-motorized vehicles, proli...

Februari 2020 merupakan waktu awal warga Indonesia didiagnosa positif Covid-19. Hingga kini, penyakit yang dikarenakan oleh Virus Corona ini belum mereda, bahkan dinyatakan sebagai Pandemi Global. Tujuan penelitian adalah mencari model prediksi Time Series untuk jumlah kasus Covid-19 di salah satu kota dengan infeksi terbsesar yaitu Jakarta. Penelitian menggunakan data dari Open Data Jakarta re...

Since gold prices influence international economic and monetary systems, numerous studies have been conducted to forecast prices. Nonetheless, employing the linear relationship method usually fail explain change in pattern of price. This study introduces a new paradigm that incorporates association rules long short-term memory (LSTM) as nonlinear-based method. For simulation, proposed was analy...