JSTOR Daily

Non-Stationary q-Dependent Processes and Time-Varying Moving-Average Models: Invertibility Properties and the Forecasting Problem

The spectral factorization problem was solved in Hallin (1984) for the class of (non-stationary) m-variate MA(q) stochastic processes, i.e. the class of second-order q-dependent processes. It was ...
InfoWorld

Time series forecasting with ARMA and InfluxDB

Autoregressive moving average models have a number of advantages including simplicity. Here’s how to use an ARMA model with InfluxDB. An ARMA or autoregressive moving average model is a forecasting ...
JSTOR Daily

On the Autocorrelation Structure for Seasonal Moving Average Models and Its Implications for the Cramér-Wold Factorization

An account is given of the autocorrelation theory of seasonal moving average models, using a vector representation defined by the author. It is shown that the autocorrelation structure of a seasonal ...
Investopedia

Master ARIMA: Your Guide to Time Series Forecasting

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...