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ARMA is appropriate when a system is a function of a series of unobserved shocks (the MA or moving average part) as well as its own behavior. For example, stock prices may be shocked by fundamental information as well as exhibiting technical trending and mean-reversion effects due to market participants.
Non-seasonal ARIMA models are usually denoted ARIMA(p, d, q) where parameters p, d, q are non-negative integers: p is the order (number of time lags) of the autoregressive model, d is the degree of differencing (the number of times the data have had past values subtracted), and q is the order of the moving-average model.
Overview of the monitor based verification process as described by Falcone, Havelund and Reger in A Tutorial on Runtime Verification. The broad field of runtime verification methods can be classified by three dimensions: [9] The system can be monitored during the execution itself (online) or after the execution e.g. in form of log analysis ...
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Verify SK (τx, σy) → y ∪ ⊥: The verification algorithm converts the worker's encoded output σy into the actual output of the function F using both the secret key SK and the secret “decoding” τx. It outputs y = F(x) if the σy represents a valid output of F on x, or outputs ⊥ otherwise.
Formally, a message authentication code (MAC) system is a triple of efficient [4] algorithms (G, S, V) satisfying: G (key-generator) gives the key k on input 1 n, where n is the security parameter. S (signing) outputs a tag t on the key k and the input string x. V (verifying) outputs accepted or rejected on inputs: the key k, the string x and ...
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where p the number of estimated parameters p and ^ is computed from the version of the model that includes all possible regressors. That concludes this proof. That concludes this proof. See also