Thus, the predictive power of machine learning models for backcasting past time-series values is also imperative. Moreover, in evaluating the performance of ML algorithms and traditional time-series models, most studies employ the fixed-origin strategy instead of a rolling-origin evaluation with ...
machine-learning algorithmsearly warningIn this study, we discuss the problem of lag- forecasting, which has been solved using a machine-learning algorithm. The main aim of this study is to define the lag- forecaster based on a precise classification approach and to discuss the technical issues ...
“what if” analysis. Compared to Traditional forecasting techniques, Machine Learning Forecasting solutions recognize the fundamental demand drivers that impact demand, exposing insights not conceivable with Traditional forecasting techniques. Besides, the self-learning algorithms get shrewder as they munch ...
The most interesting question and greatest challenge is to find the reasons for their poor performance with the objective of improving their accuracy and exploiting their huge potential. AI learning algorithms have revolutionized a wide range of applications in diverse fields and there is n...
Traditional weather forecasting is based on numerical weather prediction (NWP) algorithms, which approximately solve the equations that model atmospheric dynamics. Deterministic NWP methods map the current estimate of the weather to a forecast of how the future weather will unfold over time. To model ...
摘要: In this work, we apply cutting edge machine learning algorithms to one of the oldest challenges in finance: Predicting returns. For the sake of simplicity, we f关键词: Machine Learning ETFs Forecasting and Predicting Returns DOI: 10.2139/ssrn.2899520 被引量: 3 ...
In short, the use of forecasting models (such as those based on machine learning algorithms) to reduce FW is a topic that is still in an early stage of development. There is a need for further studies on this topic, particularly with a focus on causal models that include more diverse var...
Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural...
Machine learning techniques are proving useful for short-term electricity load forecasting. In this paper we evaluate performance of several machine learning algorithms applied to electricity load datasets. We evaluated performance of SMOreg, and Additive regression algorithms for load forecasting using ...
All details about the training procedure of the machine learning algorithms are given in the “Methods” section. Figure 8 In the plot on the left we show the performance indicators in the case of the activations prediction task. The performance on positive values improves, while the one on ...