A Deep Hybrid Model for Weather Forecasting

By Aditya Grover, Ashish Kapoor, Eric Horvitz. 2015

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In Time-Series & Forecasting

Citations in the corpus

Time-series forecasting with deep learning: a survey (2020, methodology): “p neural networks in two manners: a) to encode time-varying parameters for non-probabilistic parametric models [64, 65, 67], and b) to produce parameters of distributions used by probabilistic models [38, 40, 66]. (a) Non-probabilistic Hybrid Models With parametric time series models, forecasting eq”
Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, methodology): “(Grover et al., 2015) proposed a hybrid model using deep neural network.”
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go? (2018, background): “…(Recht et al., 2011; Yun et al., 2014; Petroni & Querzoni, 2014), to training deep neural networks (Dean et al., 2012a; Zhang et al., 2013; Paine et al., 2013; Zhang et al., 2015; Grover et al., 2015; Jiang et al., 2017) and representation learning (Grover & Leskovec, 2016; Grover et al., 2018a;b).”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, unknown): “] [239] [114] [119] [213] [276] [152] [56] [17] [238] [6] [11] [183] [182] [193] [196] [221] [113] [260] [154] [293] [232] [292] [289] [291] [246] [228] [247] [275] [258] [223] [257] [271] [153] [95] [99] [222] [105] [128] [236] [50] [156] [191] [284] [294] [255] [280] [97] [165] Parameterization [1”
ClimaX: A foundation model for weather and climate (2023, unknown): “…there has been a steady rise in data-driven approaches for forecasting of atmospheric variables, especially for meteorological applications (Grover et al., 2015; Dueben & Bauer, 2018; Weber et al., 2020; Scher & Messori, 2019; Scher, 2018; Kashinath et al., 2021; Schultz et al., 2021;…”
A Learning-Based Incentive Mechanism for Federated Learning (2020, background): “For example, the weather-related predication services [15], [16] always prefer the latest temperature and”
Deep Learning for Time-Series Analysis (2017, methodology): “Instead of Autoencoders, [20] uses Deep Belief Networks for constructing a hybrid model in which the ANN models the joint distribution between the weather predictors variables.”
Can deep learning beat numerical weather prediction? (2021, methodology): “[16], who constructed a three-stage model consisting of boosted decision trees, a dynamic Gaussian Process model, and a deep belief network consisting of restricted Boltzman machines [77,78].”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, unknown): “1) [266] [10] [219] [158] [140] [182] [203] [76] [86] [130] [174] [204] [231] [313] [121] [125] [187] [92] [306] [180] [298] [184] [267] [39] [247] [133] [130] [231] [298] [173] [66] [24] [255] [12] [17] [203] [204] [213] [239] [125] [281] [214] [176] [249] [310] [309] [311] [245] [78] [266] [297] [”
Activation Functions: Comparison of trends in Practice and Research for Deep Learning (2018, background): “derstanding and description [31], [32], machine translation [33], [34] test to speech systems [35], [36], cancer detection systems [37], [38], [39], ﬁnger print detection [40], [41], weather forecast [42], [43], self-driving cars [44], [45], and other domains to mention a few, with early research re”
Deep Learning Based Forecasting of Critical Infrastructure Data (2017, methodology): “Recently, this method has obtained promising results in weather forecasting [21].”
A Review of Deep Learning Models for Time Series Prediction (2019, methodology): “An integrated approach is proposed in [210] which combines discriminatively trained predictive models with deep neural networks accounting for the joint statistics of a set of weather-related variables.”