Results 231 to 240 of about 517,907 (286)

Performance analysis of neural network architectures for time series forecasting: A comparative study of RNN, LSTM, GRU, and hybrid models. [PDF]

MethodsX
Yunita A, Pratama MI, Almuzakki MZ, Ramadhan H, Akhir EAP, Firdausiah Mansur AB, Basori AH.   +6 more
europepmc   +1 more source

AMDCnet: attention-gate-based multi-scale decomposition and collaboration network for long-term time series forecasting. [PDF]

Front Artif Intell
Hou S, Sun S, Yin T, Zhang Z, Yan M.
europepmc   +1 more source

Evaluating the effectiveness of self-attention mechanism in tuberculosis time series forecasting. [PDF]

BMC Infect Dis
Lv Z, Sun R, Liu X, Wang S, Guo X, Lv Y, Yao M, Zhou J.   +7 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Time Series Forecasting

2012
Nowcasting global solar irradiance on very short time horizons is the principal topic discussed in this chapter. Various ARIMA models for nowcasting clearness index are inferred and assessed. Radiometric data measured at 15 s lag during June 2010 in Timisoara (Romania) are used for setting up and testing the models.
Marius Paulescu, Eugenia Paulescu, Paul Gravila, Viorel Badescu   +3 more
openaire   +1 more source

FORECASTING TIME SERIES USING WAVELETS

International Journal of Wavelets, Multiresolution and Information Processing, 2007
This paper deals with wavelets in time series, focusing on statistical forecasting purposes. Recent approaches involve wavelet decompositions in order to handle non-stationary time series in such context. A method, proposed by Renaud et al.,11 estimates directly the prediction equation by direct regression of the process on the Haar non-decimated ...
Aminghafari, Mina, Poggi, Jean-Michel
openaire   +2 more sources

Forecasting Trends in Time Series

Management Science, 1985
Most time series methods assume that any trend will continue unabated, regardless of the forecast lead time. But recent empirical findings suggest that forecast accuracy can be improved by either damping or ignoring altogether trends which have a low probability of persistence.
Everette S. Gardner, Jr., Ed. Mckenzie
openaire   +2 more sources

Time Series Forecasting

2020
Forecasting has always been a topic of interest for every human being. What is my future? Will I become a millionaire in the next 5 years? When will I get married? These are the questions raised by several of us. There are people in this world who do forecasting and at least try to provide answers to such questions.
openaire   +1 more source

Forecasting Time Series

2011
This chapter includes two problems for forecasting of a time series using past data points. It is argued that the past data points used for forecasting of the future data points should be strongly correlated with each other. It is illustrated that the strongly correlated past data points can be identified from the autocorrelation function of the time ...
openaire   +1 more source

Forecasting big time series

Proceedings of the VLDB Endowment, 2018
Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future demand in different regions; in cloud computing, the estimated future usage of services and infrastructure components guides capacity planning; and ...
Christos Faloutsos, Jan Gasthaus, Tim Januschowski, Yuyang Wang   +3 more
openaire   +1 more source