Forecasting power-transformed time series data
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When there is an interest in forecasting the growth rates as well as the levels of a single macro-economic time series, a practitioner faces the question of whether a forecasting model should be constructed for growth rates, for levels, or for both. In this paper, we investigate this issue for 10 US (un-)employment series, where we evaluate the forecasts from a non-linear time series model for power-transformed data. Our main finding is that models for growth rates (levels) do not automatically result in the most accurate forecasts of growth rates (levels).Keywords:
Macro
Predictive power
Seasonality
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This chapter contains sections titled: Nicholson's Blowflies Moving Average Seasonal Data Built-in Time Series Functions Decompositions Testing for a Trend in the Time Series Spectral Analysis Multiple Time Series Simulated Time Series Time Series Models Time series modelling on the Canadian lynx data
Cross-spectrum
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In the present study we examine the predictive power of disagreement amongst forecasters. In our empirical work, we find that in some situations this variable can signal upcoming structural and temporal changes in an economic process and in the predictive power of the survey forecasts. We examine a variety of macroeconomic variables, and we use different measurements for the degree of disagreement, together with measures for location of the survey data and autoregressive components. Forecasts from simple linear models and forecasts from Markov regime‐switching models with constant and with time‐varying transition probabilities are constructed in real time and compared on forecast accuracy. Copyright © 2015 John Wiley & Sons, Ltd.
Predictive power
Value (mathematics)
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According to the inner characters of the time series, the conception of time-series trending structure series and the latest time sub-series was defined, and the time-series trending structure mining technique was presented. The technique first converted the time series waiting for mining into its trending structure series, then used the information underlying in the latest trending structure sub-series of the latest time sub-series as a guide to excavate the original time series. The model of RBF neural network was also presented to predict the trend of time series with real world data set. Experimental results reveal that the time-series trending structure mining technique is significant
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Abstract In this paper a data analysis tool for analyzing highly correlated time series data is suggested. The main objective is to unify multiple time series into a single series and then apply a univariate method for the purpose of prediction. This method is essentially efficient for analyzing multiple time series with sparse data. Several time series data of relative demand for black and white television receivers in various countries are analyzed and quite accurate predictions are obtained.
Univariate
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WordPerfect version 5.0 has been widely praised for its enhancements with programmable macros. Based on a tradition of macro support since version 2.23, WordPerfect 5.0 allows users to manipulate text in ways previously possible only with more advanced programming languages. The new version, for example, allows for the assignment of variables, conditional testing, advanced looping, sub‐routines, and error handling. It also includes a macro editor as part of the basic software package. With the editor, you can easily modify existing macros—a feature particularly useful with large macros created with the new programming features. In this article I will provide a basic introduction to these macro capabilities and their uses. I have also designed a simple accessions list macro ( ACCLIST ) that demonstrates some of the new features. A later article will illustrate more complex possibilities.
Macro
Feature (linguistics)
Macro level
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With interface macros you can perform actions such as opening another view, applying a filter, or creating a new record. There are two kinds: “embedded” UI macros, which attach directly to user interface objects such as command buttons, combo boxes, or the Action Bar button object, and “standalone” UI macros, contained in macro objects. To avoid duplicating code, reuse standalone UI macros by calling them from other macros. You can see the standalone UI macros in the Navigation Pane, under Macros, but you can’t run them directly from there. Use the Run Macro action to run a standalone UI macro from an embedded UI macro
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Micro level
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This chapter specifically covers data macros, which add yet another reason that macros in Access 2019 are a more attractive option than ever before. A data macro is logic users attach to a table to enforce business rules at the table level. Data macros are intended to make it easier to ensure consistent data handling throughout their application. The chapter discusses five different macro-programmable table events: BeforeChange, BeforeDelete, AfterInsert, AfterUpdate, and AfterDelete. Data macros use the same macro builder used to create embedded and user interface macros. The Action Catalog on the right side of the macro builder serves as the repository of macro actions the users add to their data macros. Data macros are attached directly to Access tables and not to individual fields. If the users have a situation where more than a few fields must be monitored or updated, the macro may become quite complex.
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Table (database)
Data access
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