Results 11 to 20 of about 50,820 (258)

Nyström Regularization for Time Series Forecasting

CoRR, 2021
This paper focuses on learning rate analysis of Nyström regularization with sequential sub-sampling for $τ$-mixing time series. Using a recently developed Banach-valued Bernstein inequality for $τ$-mixing sequences and an integral operator approach based on second-order decomposition, we succeed in deriving almost optimal learning rates of Nyström ...
Zirui Sun, Mingwei Dai, Yao Wang 0003, Shao-Bo Lin   +3 more
openaire   +4 more sources

A Time Series Forecasting Method

ITM Web of Conferences, 2017
This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally.
Wang Zhao-Yu, Lin Yu-Chun, Lee Shie-Jue, Lai Chih-Chin   +3 more
doaj   +1 more source

Probabilistic-Based Forecasting Method For Time Series Datasets

Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2023
In this paper, a new probabilistic technique (a variant of Multiple Model Particle Filter-MMPF) will be used to predict time-series datasets. At first, the reliable performance of our method is proved using a virtual random scenario containing sixty ...
Abdullatif Baba
doaj   +1 more source

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 Techniques for Univariate Time Series Data: Analysis and Practical Applications by Category

Computer Sciences & Mathematics Forum
Effective forecasting is vital in various domains as it supports informed decision-making and risk mitigation. This paper aims to improve the selection of appropriate forecasting methods for univariate time series.
Leonard Dervishi, Antonios Raptakis, Gerald Bieber   +2 more
doaj   +1 more source

Forecasting Randomly Distributed Zero-Inflated Time Series

Folia Oeconomica Stetinensia, 2017
The main aim of the article is to propose a forecasting procedure that could be useful in the case of randomly distributed zero-inflated time series.
Doszyń Mariusz
doaj   +1 more source

Unilateral boundary time series forecasting

Frontiers in Big Data
Time series forecasting is an essential tool across numerous domains, yet traditional models often falter when faced with unilateral boundary conditions, where data is systematically overestimated or underestimated. This paper introduces a novel approach
Chao-Min Chang, Cheng-Te Li, Shou-De Lin
doaj   +1 more source

Entanglement-Structured LSTM Boosts Chaotic Time Series Forecasting

Entropy, 2021
Traditional machine-learning methods are inefficient in capturing chaos in nonlinear dynamical systems, especially when the time difference Δt between consecutive steps is so large that the extracted time series looks apparently random.
Xiangyi Meng, Tong Yang
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
doaj   +1 more source