We explore how information additional to a specific price series can be used improve the power of popular univariate autoregressive-based methods for detecting and dating speculative bubble episodes. Following Phillips et al. (2011, 2015) we base our approach on sequences sub-sample regression-based augmented Dickey–Fuller [ADF] statistics. Our point departure from these extant procedures is al...

Abstract The article aims to present the methodology of estimating atmospheric water exchange components in lake. In absence direct precipitation and evaporation measurements, these balance elements need be estimated. However, inadequate selection estimation methods causes incorrect determination hydrological function lake effect it has on formation river drainage. Determination from lake’s sur...

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...

introduction the knowledge about spatial variability of precipitation is a key issue for regionalization in hydro-climatic studies. measurements of meteorological parameters by the traditional methods require a dense rain gauge network. but, due to the topography and cost problems, it is not possible to create such a network in practice. in these cases the spatial distribution pattern of precip...

Existing data on COPD prevalence are limited or totally lacking in many regions of Europe. The geographic information system inverse distance weighted (IDW) interpolation technique has proved to be an effective tool in spatial distribution estimation of epidemiological variables, when real data are few and widely separated. Therefore, in order to represent cartographically the prevalence of COP...

Maximum consensus estimation plays a critically important role in several robust fitting problems computer vision. Currently, the most prevalent algorithms for maximization draw from class of randomized hypothesize-and-verify algorithms, which are cheap but can usually deliver only rough approximate solutions. On other extreme, there exact exhaustive search nature and be costly practical-sized ...