Avalanche forecasting relies on snow, snow cover and weather data - for expert evaluation as well as for machine learning based support tools. The data need to be accessible in high quality as soon as these become available. Any measurement errors, anomalies and data gaps diminish forecast accuracy. The objective is the development of algorithms that allow real-time detection of anomalies in the time series, but also the detection of outliers, and impute missing data by applying state-of-the-art machine learning approaches. This real-time data cleansing will solve a long-standing issue with the IMIS data that are known to be contaminated with data anomalies and has hindered automated processing. Hence, the completion of the proposed research will have a major impact, in particular for the application of numerical avalanche prediction models such as we recently developed in collaboration with the SDSC.