This article discusses Time-MOE, an open-source time-series foundation model using Mixture-of-Experts (MOE) to improve forecasting accuracy while reducing computational costs. Key contributions include the Time-300B dataset, scaling laws for time series, and the Time-MOE architecture.
A deep dive into time series analysis and forecasting methods, providing foundational knowledge and exploring various techniques used for understanding past data and predicting future outcomes.
The article discusses an interactive machine learning tool that enables analysts to interrogate modern forecasting models for time series data, promoting human-machine teaming to improve model management in telecoms maintenance.
This article explores TimeMixer, a new time series forecasting model, and its implementation. The article delves into its inner workings and provides a benchmark comparison with other models.
A powerful library by Amazon — coding example included
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):
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):
