Non-stationary demand forecasting by cross-sectional aggregation

Forecasting non-stationary time series by wavelet process modelling

Many time series in the applied sciences display a time-varying second order structure. In this article, we address the problem of how to forecast these non-stationary time series by means of non-decimated wavelets. We first consider a model in which only the variance evolves with time. We define a predictor for this model and show an application to the Dow Jones index. Then, we generalise the ...

Analysis of a Forecasting-Production-Inventory System with Stationary Demand

Forecasting Non - stationary Economic Time Series

Forecasting macroeconomic time series is notoriously difficult. Previously unannounced changes in policy, natural and man-made disasters, institutional changes, new discoveries, new data definitions and revisions among others cause occasional large forecast errors in the standard constant-parameter models. While much effort has been put into working out the theory of estimation, inference and i...

Forecasting Non-Stationary Volatility with Hyper- Parameters

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Temporal Aggregation of Stationary and Non-stationary Continuous-Time Processes

We study the autocorrelation structure of aggregates from a continuous-time process. The underlying continuous-time process or some of its higher derivative is assumed to be a stationary continuous-time auto-regressive fractionally integrated moving-average (CARFIMA) process with Hurst parameter H. We derive closed-form expressions for the limiting autocorrelation function and the normalized sp...