The time-series forecasting makes a substantial contribution in timely decision-making. In this article, recently developed eigenvalue decomposition of Hankel matrix (EVDHM) along with the autoregressive integrated moving average (ARIMA) is applied to develop model for nonstationary time series. Phillips–Perron test (PPT) used define nonstationarity EVDHM over series decompose it into respectiv...

In this paper, by using of laser induced breakdown spectroscopy (LIBS) method, the elemental concentrations existed in standard aluminum alloys are measured quantitatively. Pulse Nd:YAG at 1064 nm is irradiated on Al samples and analysis performed created plasma. Among different methods for estimation concentration elements samples, artificial neural network (ANN), Support vector regression, au...

In this study, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied to predict the monthly flow time series of two stations, named Begova Chemcermo, for Khabour River. The analysis was performed using several criteria tests. autocorrelation function (ACF) partial (PACF) check accuracy ARIMA model. Akaike (AIC) Bayesian (BIC) equations determin...

Forecasting hierarchical or grouped time series usually involves two steps: computing base forecasts and reconciling the forecasts. Base can be computed by popular forecasting methods such as Exponential Smoothing (ETS) Autoregressive Integrated Moving Average (ARIMA) models. The reconciliation step is a linear process that adjusts to ensure they are coherent. However using ETS ARIMA for comput...

Peramalan merupakan salah satu bidang penelitian yang aktif artinya sampai saat ini masih terus dilakukan mengenai proses peramalan runtun waktu terkait dengan pengambilan keputusan. Metode berkembang menjadi semakin cepat mengikuti perkembangan zaman dan teknologi komputasi. Terdapat hal menarik dari tersebut ialah perbaikan metode bersifat hybrid, menggabungkan dua jenis atau lebih berbeda, d...

It is well-known that causal forecasting methods that include appropriately chosen Exogenous Variables (EVs) very often present improved forecasting performances over univariate methods. However, in practice, EVs are usually difficult to obtain and in many cases are not available at all. In this paper, a new causal forecasting approach, called Wavelet Auto-Regressive Integrated Moving Average w...