Results 81 to 90 of about 1,891,060 (279)
A note on prediction and interpolation errors in time series [PDF]
, 2004 In this note we analyze the relationship between one-step ahead prediction errors and interpolation errors in time series. We obtain an expression of the prediction errors in terms of the interpolation errors and then we show that minimizing the sum of ...
Galeano, Pedro, Peña, Daniel
core +1 more source
Deep Learning with Long Short-Term Memory for Time Series Prediction
, 2018 Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms,
Chen, Xianfu +5 more
core +1 more source
Molecular Oncology, EarlyView.Loss of the miR‐214/199a cluster is associated with recurrence in ovarian cancer. Engineered small extracellular vesicles (m214‐sEVs) elevate miR‐214‐3p/miR‐199a‐5p in tumor cells, suppress β‐catenin, TLR4, and YKT6 signaling, reprogram tumor‐derived sEV cargo, reduce chemoresistance and migration, and enhance carboplatin efficacy and survival in ...
Weida Wang +12 more
wiley +1 more source
New in-sample prediction errors in time series with applications [PDF]
, 2001 ^aThis article introduces two new types of prediction errors in time series: the filtered prediction errors and the deletion prediction errors. These two prediction errors are obtained in the same sample used for estimation, but in such a way that they ...
Peña, Daniel, Sánchez, Ismael
core +1 more source
Molecular Oncology, EarlyView.Keratin 19 (KRT19) is overexpressed in high‐grade serous ovarian cancer with high levels of Kallikrein‐related peptidases (KLK) 4–7 and is associated with poor survival. In vivo analyses demonstrate that elevated KRT19 increases peritoneal tumour burden.
Sophia Bielesch +13 more
wiley +1 more source
Semiparametric Bootstrap Prediction Intervals in time Series
پژوهشهای ریاضی, 2015 One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals ...
N. Iranpanah, P Mikelani
doaj
Prediction of Multivariate Chaotic Time Series using GRU, LSTM and RNN
Sakarya University Journal of Computer and Information SciencesChaotic systems are identified as nonlinear, deterministic dynamic systems that are exhibit sensitive to initial values. Some chaotic equations modeled from daily events involve time information and generate chaotic time series that are sequential data ...
Osman Eldoğan, Gülyeter Öztürk
doaj +1 more source
Molecular Oncology, EarlyView.We have established a humanized orthotopic patient‐derived xenograft (Hu‐oPDX) mouse model of high‐grade serous ovarian cancer (HGSOC) that recapitulates human tumor–immune interactions. Using combined anti‐PD‐L1/anti‐CD73 immunotherapy, we demonstrate the model's improved biological relevance and enhanced translational value for preclinical ...
Luka Tandaric +10 more
wiley +1 more source
Improving demand forecasting with LSTM by taking into account the seasonality of data
Journal of Applied Research on Industrial Engineering, 2020 Demand forecasting is a vital task for firms to manage the optimum quantity of raw material and products. The demand forecasting task can be formulated as a time series forecasting problem by measuring historical demand data at equal intervals.
Hossein Abbasimehr +1 more
doaj +1 more source
CCDC80 suppresses high‐grade serous ovarian cancer migration via negative regulation of B7‐H3
Molecular Oncology, EarlyView.PAX8 is a lineage‐specific master regulator of transcription in high‐grade serous ovarian cancer (HGSC) progression. We show for the first time that PAX8 facilitates proliferation and metastasis by repressing the cell autonomous tumor suppressor CCDC80 and inducing B7‐H3 expression.
Aya Saleh +12 more
wiley +1 more source