Programs TRAMO and SEATS, that contain an ARIMA-model-based methodology, are applied for seasonal adjustment and trend-cycle estimation of the exports, imports, and balance of trade Japanese series. The programs are used in an automatic mode, and the results are found satisfactory. It is shown how the SEATS output can be used to discriminate among competing models. Finally, using the balance of...

Nha Trang Coast is located in the South Central Vietnam and coastal erosion has occurred rapidly recent years. Hence it crucial to accurately monitor shoreline changes for better management reduction of risks communities. In this paper, we explored a statistical forecasting model, Seasonal Auto-regressive Integrated Moving Average (SARIMA), two Machine Learning (ML) models, Neural Network Auto-...

Semantic Differencing for Numerical Programs Nimrod Partush and Eran Yahav

Firms use time-series forecasting methods to predict sales. However, it is still a question which method forecaster best, if only single forecast needed. This study investigates and evaluates different sales methods: multiplicative Holt-Winters (HW), additive HW, seasonal auto regressive integrated moving average (SARIMA) [a variant of (ARIMA)], long short-term memory (LSTM) recurrent neural ne...

Nonstationary ARIMA processes and nearly nonstationary ARMA processes, such as autoregressive processes having a root of the AR polynomial close to the unit circle, have sample autocovariance and spectral properties that are, in practice, almost indistinguishable from those of a stationary longmemory process, such as a Fractionally Integrated ARMA (ARFIMA) process. Because of this, model misspe...

In this paper we examine the ̄nite-sample properties of the approximate maximum likelihood estimate (MLE) of the fractional di®erencing parameter d in an ARFIMA(p, d, q) model based on the wavelet coe±cients. Ignoring wavelet coe±cients of higher order of resolution, the remaining wavelet coe±cients approximate a sample of independently and identically distributed normal variates with homogeneo...

This paper provides a Bayesian analysis of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. We discuss in detail inference on impulse responses, and show how Bayesian methods can be used to (i) test ARFIMA models against ARIMA alternatives, and (ii) take model uncertainty into account when making inferences on quantities of interest. Our methods are then used to investigat...

energy price forecast is the key information for generating companies to prepare their bids in the electricity markets. however, this forecasting problem is complex due to nonlinear, non-stationary, and time variant behavior of electricity price time series. accordingly, in this paper a new strategy is proposed for electricity price forecast. the forecast strategy includes wavelet transform (wt...

Indonesia's price index serves as a barometer for the nation's economic condition. One of Indonesia’s is Wholesale Price Index (WPI). WPI that tracks average change in wholesale prices over time. Time series analysis can be used forecasting because one time data. long memory, which condition data from different periods have high link despite being separated by large amount The Autoregressive Fr...