The paper provides general matrix formulas for minimum mean squared error signal extraction, for a finitely sampled time series whose signal and noise components are nonstationary ARIMA processes. These formulas are quite practical; as well as being simple to implement on a computer, they make it possible to easily derive important general properties of the signal extraction filters. We also ex...

We propose a hybrid forecast based on extended discrete grey Markov and variable dimension Kalman model and show that our hybrid model can improve much more the performance of forecast than traditional grey Markov and Kalman models. Our simulation results are given to demonstrate that our hybrid forecast method combined with degree of grey incidence are better than grey Markov and ARFIMA model ...

The purpose of the paper is to extend recent results of Muellbauer (1986) for the life cycle consumption model under rational habit formation. It is shown that a general pattern of habits will lead to an arbitrary autoregressive integrated moving average (ARIMA) process for consumption. As a special case it is shown how a model in annual differences can be obtained by imposing a specific struct...

This letter improves two adaptive steganographic methods in Refs. [5], [6], which utilize the remainders of two consecutive pixels to record the information of secret data. Through analysis, we point out that they perform mistakenly under some conditions, and the recipient cannot extract the secret data exactly. We correct these by enlarging the adjusting range of the remainders of two consecut...

This paper considers the time series properties of the aggregate price level which is well known as one of the most important variables in explaining the macroeconomy. Many studies have been devoted to investigate how aggregate prices respond to shocks. Nelson and Schwert (1977), Barsky (1987), and Ball and Cecchetti (1990) find that inflation contains a unit root. Hassler and Wolters (1995) an...

Time-series data with regular and/or seasonal long-memory are often aggregated before analysis. Often, the aggregation scale is large enough to remove any short-memory components of the underlying process but too short to eliminate seasonal patterns of much longer periods. In this paper, we investigate the limiting correlation structure of aggregate time series within an intermediate asymptotic...

This paper considers the application of long memory processes to describe inflation with seasonal behaviour. We use three different long memory models taking into account the seasonal pattern in the data. Namely, the ARFIMA model with deterministic seasonality, the ARFISMA model, and the periodic ARFIMA (PARFIMA) model. These models are used to describe the inflation rates of four different cou...

Current XML differencing applications usually try to find a minimal sequence of edit operations that transform one XML document to another XML document (the so-called "edit script"). In our conviction, this approach often produces increments that are unintuitive for human readers and do not reflect the actual changes. We therefore propose in this article a different approach trying to maximise ...

The electric power load forecasting is critical for stable electric power system supply. In this paper, a seasonal ARIMA model was used to effectively forecast power load data characterized using periodicity. A numerical example reveals that the seasonal ARIMA model effectively forecast periodic power load.