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Forecasting Time Series with Boot.EXPOS Procedure

Revstat Statistical Journal, 2009
To forecast future values of a time series is one of the main goals in times series analysis. Many forecasting methods have been developed and its performance evaluated.
Clara Cordeiro , M. Manuela Neves
doaj   +1 more source

Forecasting Economic Time Series

, 1998
This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz.
Clements, M, Hendry, D
openaire   +2 more sources

A Seasonal Autoregressive Integrated Moving Average (SARIMA) Model to Forecasting Tourist Arrival in the Philippines: A Case Study in Moalboal, Cebu (Philippines)

Recoletos Multidisciplinary Research Journal, 2020
Forecasting plays a critical part in implementing effective tourism management strategies. However, the role of tourism forecasting is not extensively studied in the Philippines, which is a key tourism destination in Southeast Asia.
Severina P. Velos, Marivel B. Go, Glynne P. Bate, Elvira B. Joyohoy   +3 more
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Dense Sampling of Time Series for Forecasting

IEEE Access, 2022
A time series contain a large amount of information suitable for forecasting. Classical statistical and recent deep learning models have been widely used in a variety of forecasting applications.
Il-Seok Oh, Jin-Seon Lee
doaj   +1 more source

With string model to time series forecasting

, 2015
Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real ...
Bartoš, Erik, Pinčák, Richard
core   +1 more source

TIME SERIES FORECASTING BY THE ARIMA METHOD

Scientific Journal of Astana IT University, 2022
The variety of communication services and the growing number of different sensors with the appearance of IoT (Internet of Things) technology generate significantly different types of network traffic.
Gulnara Bektemyssova, Abdul Rahim Ahmad, Sharafat Mirzakulova, Zhanar Ibraeva   +3 more
doaj   +1 more source

Forecasting With Nonlinear Time Series Models [PDF]

SSRN Electronic Journal, 2010
AbstractThis article considers nonlinear forecasting models, such as switching-regime models. These models are typically “small” compared to vector autoregressive and factor models, being either univariate or single-equation models, but tend to nest a linear relationship and so invite an assessment of whether allowing for nonlinearity improves forecast
Kock, Anders Bredahl, Teräsvirta, Timo
openaire   +3 more sources

Robust Multi-Dimensional Time Series Forecasting

Entropy
Large-scale and high-dimensional time series data are widely generated in modern applications such as intelligent transportation and environmental monitoring. However, such data contains much noise, outliers, and missing values due to interference during
Chen Shen, Yong He, Jin Qin
doaj   +1 more source

Fuzzy Supervised Multi-Period Time Series Forecasting

Cybernetics and Information Technologies, 2019
The goal of this paper is to propose a new method for fuzzy forecasting of time series with supervised learning and k-order fuzzy relationships. In the training phase based on k previous historical periods, a multidimensional matrix of fuzzy dependencies
Ilieva Galina
doaj   +1 more source

Time-Series Forecasting [PDF]

Significance, 2005
Abstract As we go through life, everyone makes forecasts all the time, often without realising it. Sadly these forecasts are often (very) inaccurate. Chris Chatfield looks at the chequered history of forecasting and asks how we might do it better using time-series data, and what statistical techniques and models might help us.
openaire   +1 more source