Open Source libarary for forecasting in Python - this has implementation of all traditional methods and newer ones such as - 1. Theta Method 2. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

Open Source libarary for forecasting in Python - this has implementation of all traditional methods and newer ones such as - 1. Theta Method 2. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.  read less

The healthcare industry requires accuracy and highly interpretable models. Depending on the application domain in healthcare (clinical versus operational), the models can range from forecasting to segmentation and clustering to classification and regression, including areas like natural language processing (NLP) and image processing. However, the data is usually plagued by missing information and incorrect values, so data preparation and devel... read more

The healthcare industry requires accuracy and highly interpretable models. Depending on the application domain in healthcare (clinical versus operational), the models can range from forecasting to segmentation and clustering to classification and regression, including areas like natural language processing (NLP) and image processing. However, the data is usually plagued by missing information and incorrect values, so data preparation and developing a good model with interpretability are both time-consuming tasks. Enter AutoML and auto-model interpretability. Taposh DuttaRoy and Sabrina Dahlgren discuss tools and strategies for AutoML and interpretability and explain how Kaiser Permanente uses them to improve time to develop and deploy highly interpretable models. Use cases include: The creation of patient journeys for specific diseases stages and finding probability at each stage Determining high utilizers across multiple years (a classification problem) Forecasting census for operations reporting  read less

Current main stream data science work is mainly focused on prediction , segmentation and data analysis. This mainly involves supervised learning where we learn from historic data. The predicted observations usually gives a probability score which measures randomness. We believe there are situations where along with randomness we need some fuzziness to give some confidence to the observation. Fuzziness and randomness are two very different conc... read more

Current main stream data science work is mainly focused on prediction , segmentation and data analysis. This mainly involves supervised learning where we learn from historic data. The predicted observations usually gives a probability score which measures randomness. We believe there are situations where along with randomness we need some fuzziness to give some confidence to the observation. Fuzziness and randomness are two very different concepts. We say that fuzziness occurs when there is no boundary between outcomes. And we commonly refer to randomness as the uncertainty associated with effective variability from alternative outputs. Although different, it is simple to provide real world scenarios where randomness and fuzziness work together. One example is in the stock market where there are no historical similarities to use in modeling such as new high patterns. Here, we say that there are no defined boundaries that forecast event probabilities in the next few days.  read less