4,783 research outputs found
Does money matter in inflation forecasting?
This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.Forecasting ; Inflation (Finance) ; Monetary theory
Forecast combinations: an over 50-year review
Forecast combinations have flourished remarkably in the forecasting community
and, in recent years, have become part of the mainstream of forecasting
research and activities. Combining multiple forecasts produced from single
(target) series is now widely used to improve accuracy through the integration
of information gleaned from different sources, thereby mitigating the risk of
identifying a single "best" forecast. Combination schemes have evolved from
simple combination methods without estimation, to sophisticated methods
involving time-varying weights, nonlinear combinations, correlations among
components, and cross-learning. They include combining point forecasts and
combining probabilistic forecasts. This paper provides an up-to-date review of
the extensive literature on forecast combinations, together with reference to
available open-source software implementations. We discuss the potential and
limitations of various methods and highlight how these ideas have developed
over time. Some important issues concerning the utility of forecast
combinations are also surveyed. Finally, we conclude with current research gaps
and potential insights for future research
Time series prediction and forecasting using Deep learning Architectures
Nature brings time series data everyday and everywhere, for example, weather data, physiological signals and biomedical signals, financial and business recordings. Predicting the future observations of a collected sequence of historical observations is called time series forecasting. Forecasts are essential, considering the fact that they guide decisions in many areas of scientific, industrial and economic activity such as in meteorology, telecommunication, finance, sales and stock exchange rates. A massive amount of research has already been carried out by researchers over many years for the development of models to improve the time series forecasting accuracy. The major aim of time series modelling is to scrupulously examine the past observation of time series and to develop an appropriate model which elucidate the inherent behaviour and pattern existing in time series. The behaviour and pattern related to various time series may possess different conventions and infact requires specific countermeasures for modelling. Consequently, retaining the neural networks to predict a set of time series of mysterious domain remains particularly challenging. Time series forecasting remains an arduous problem despite the fact that there is substantial improvement in machine learning approaches. This usually happens due to some factors like, different time series may have different flattering behaviour. In real world time series data, the discriminative patterns residing in the time series are often distorted by random noise and affected by high-frequency perturbations. The major aim of this thesis is to contribute to the study and expansion of time series prediction and multistep ahead forecasting method based on deep learning algorithms. Time series forecasting using deep learning models is still in infancy as compared
to other research areas for time series forecasting.Variety of time series data has been considered in this research. We explored several deep learning architectures on
the sequential data, such as Deep Belief Networks (DBNs), Stacked AutoEncoders (SAEs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks
(CNNs). Moreover, we also proposed two different new methods based on muli-step ahead forecasting for time series data. The comparison with state of the art methods is also exhibited. The research work conducted in this thesis makes theoretical, methodological and empirical contributions to time series prediction and multi-step ahead forecasting by using Deep Learning Architectures
Hierarchical Ensemble-Based Feature Selection for Time Series Forecasting
We study a novel ensemble approach for feature selection based on
hierarchical stacking in cases of non-stationarity and limited number of
samples with large number of features. Our approach exploits the co-dependency
between features using a hierarchical structure. Initially, a machine learning
model is trained using a subset of features, and then the model's output is
updated using another algorithm with the remaining features to minimize the
target loss. This hierarchical structure allows for flexible depth and feature
selection. By exploiting feature co-dependency hierarchically, our proposed
approach overcomes the limitations of traditional feature selection methods and
feature importance scores. The effectiveness of the approach is demonstrated on
synthetic and real-life datasets, indicating improved performance with
scalability and stability compared to the traditional methods and
state-of-the-art approaches
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