1992b. "State Space Modeling of Non-Standard Ac- tuarial Time Series." Insurance: Mathematics and Eco- nomics 11:209 -22.B P Carlin. State Space Modeling of Non-Standard Actuarial Time Series[J], Insur. Math. Econ., 1992,(11).
The study of state–space models has had a profound impact ontime seriesanalysis. A linear state–space model for a (possibly multivariate) time series {Yt,t= 1, 2, …} consists of two equations. The first, known as the observation equation, expresses thew-dimensionalobservation vectorYtas ...
Deep State Space Models for Time Series Forecasting（NIPS 2018）提出了以RNN为基础的Deep State Space...
State Space Modeling and Conditional Mode Estimation for Categorical Time Series - Fahrmeir - 1992 () Citation Context ...able bias: β̂2 − β2 = Cov(X2, X3)V ar(X2)β3 V ar(X2) = 1. (9) In this sense, NB-I(1) has a correctly biased estimate closer to what it ...
Code for SpaceTime, a neural net architecture for time series. Named after state-space models for time series forecasting and classification. Cousin of S4, S4D, DSS, and H3. Descendent of LSSL. Expressive autoregressive modeling + fast flexible decoding (i.e., forecasting) ftw. Proposed in ...
Linear Time Invariant (LTI) state space models are a linear representation of a dynamic system in either discrete or continuous time. Putting a model into state space form is the basis for many methods in process dynamics and control analysis. Below is the continuous time form of a model in...
[2024.04.15] We release the first version of the survey on state space model [arXiv] Video Tutorial Mamba: Linear-Time Sequence Modeling with Selective State Spaces (COLM Oral 2024) Thesis & Surveys Modeling sequences with structured state spaces, Responsibility: Albert Gu, Publication: [Stan...
State space form in Computer Science refers to a representation of a system's complete status at any given time using matrices to describe state evolution constraints, allowing for interpretation of the plant's state based on inputs and decisions made. ...
Dorfman, J. H. (1997): "Competing exchange rate models: a state space model vs struc- tural time series alternatives," in Applications of computer aided time series modeling. Lecture Notes in Statistics, Vol. 119, ed. by M. Aoki, and A. M. Havenner. Springer, New York....
State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years. These models approximate continuous systems using a set of basis functions and discretize them to handle input data, making them well-suited for modeling time series data collected at specific freq...