klotz: time-series*

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  1. - A machine learning framework developed by Monash University and Ant Group.
    TIME-LLM repurposes Large Language Models (LLMs) for time series forecasting without modifying their core structure.

    - The innovative reprogramming technique called Prompt-as-Prefix (PaP) translates time series data into text prototypes, allowing LLMs to interpret and predict time series data accurately.
    TIME-LLM demonstrates superior performance in both few-shot and zero-shot learning scenarios compared to specialized forecasting models across various benchmarks.
    The success of TIME-LLM opens up new avenues for applying LLMs in data analysis and beyond, as it shows that they can be effectively repurposed for tasks outside their original domain.
  2. Explore the architecture of TiDE and apply it in a forecasting project using Python
    2024-01-09 Tags: , , , by klotz
  3. Nvidia Researchers Developed and Open-Sourced a Standardized Machine Learning Framework for Time Series Forecasting

    Nvidia researchers have developed and open-sourced a standardized machine learning framework called TSPP (Time Series Prediction Platform) for time series forecasting. The framework is des
    igned to facilitate the integration and comparison of various models and datasets, covering all aspects of the machine learning process from data handling to model deployment.

    The TSPP framework includes critical components like data handling, model design, optimization, and training, as well as inference, predictions on unseen data, and a tuner component that s
    elects the top configuration for post-deployment monitoring and uncertainty quantification. The methodology of TSPP is comprehensive, covering all aspects of the machine learning process.
    2024-01-05 Tags: , , by klotz
  4. Building on ideas from Meta’s Prophet package to create powerful features for time series machine learning models
    2023-10-15 Tags: , , by klotz
  5. techniques may perform well, it is rarely the case, so you need a few backup.

    Identifying the Type of Missingness

    The first step to implementing an effective imputation strategy is identifying why the values are missing. Even though each case is unique, missingness can be grouped into three broad categories:

    Missing Completely At Random (MCAR): this is a genuine case of data missing randomly. Examples are sudden mistakes in data entry, temporary sensor failures, or generally missing data that is not associated with any outside factor. The amount of missingness is low.

    Missing At Random (MAR): this is a broader case of MCAR. Even though missing data may seem random at first glance, it will have some systematic relationship with the other observed features — for example — data missing from observational equipment during scheduled maintenance breaks. The number of null values may vary.

    Missing Not At Random (MNAR):

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