Here are some recent papers about time series models in machine learning. These represent the state-of-the-art, and they show that such models can forecast a variety of events with promising accuracy. In my research, I use models like these to predict future crop yields. The Earth's changing climate presents a challenge to traditional methods of forecasting. My research aims to mitigate this effect of climate change:

• Drought modelling based on artificial intelligence and neural network algorithms: a case study in Queensland, Australia by K. Dayal et. al - Uses AI to predict drought indices with a focus on mitigating climate change
• A Deep Gaussian Process for Forecasting Crop Yield and Time Series Analysis of Precipitation Based in Munshiganj, Bangladesh by MD Mehdi et. al - Proposes a time series analysis of rainfall using deep learning (DL) architectures known as the convolutional neural network (CNN) and long short term memory (LSTM) network to predict yields for the potato crop in Bangladesh
• An artificial intelligence approach to prediction of corn yields under extreme weather conditions using satellite and meteorological data by N. Kim et. al - Shows that AI can provide robust predictions for crop yields even during anomalous conditions such as those linked to climate change, like extreme heat and cold, and drought and heavy rainfall
• Machine learning methods for crop yield prediction and climate change impact assessment in agriculture by A. Crane-Droesch - Reports good results using DNNs to predict corn crop yields during extreme weather anomalies linked to climate change within U.S. corn belt states
• A scalable machine learning system for pre-season agriculture yield forecast by I. Oliveira et. al - Adds further credibility to the notion that DNNs incorporating LSTM layers are an effective architectural style for the task of predicting future crop yields based on previous years’ data
• Random forests for global and regional crop yield predictions by JH Jeong et. al - Acknowledges the limitations of RF in predicting future yields in a changing climate and the need for more research in this area
• Strategic planning for drought mitigation under climate change by X. Cai et. al - Emphasizes a critical need for agricultural strategies to deal with climate change and drought, works with general circulation models (GCMs) and regional climate models (RCMs)
• Machine learning and artificial intelligence to aid climate change research and preparedness by C. Huntingford et. al - Suggests that integrating AI and ML with features and diagnostic data from the Earth System Model would help
• Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology by JT Abotzoglou - Presents simulated future data useful for testing