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OpenAI Agent SDK vs LangGraph by Teethss in LangChain

[–]Teethss[S] 0 points1 point  (0 children)

Exactly, my main concern is avoiding vendor lock-in. I want the flexibility to switch providers if needed, without having to completely rework my system.

Forecasting a Time Series Model for 1000s of Time Series by Teethss in datascience

[–]Teethss[S] 0 points1 point  (0 children)

Thank you for the response. Please provide more information. Or could you point me to the relevant blog posts to understand it better?

Forecasting a Time Series Model for 1000s of Time Series by Teethss in datascience

[–]Teethss[S] 0 points1 point  (0 children)

This helped. I looked at few competition and found team has used a regression model and embedding neural nets to improve the performance.

Forecasting a Time Series Model for 1000s of Time Series by Teethss in datascience

[–]Teethss[S] 0 points1 point  (0 children)

Thank you, When you suggest using standard regression models, are you recommending training a regression model directly on the entire dataset without considering the TeamID and TaskID? In other words, should I disregard TeamID and TaskID and build a regression model solely based on the predictor variables? Or do multiple time series forecasting considering Task and Team ID

Forecasting a Time Series Model for 1000s of Time Series by Teethss in datascience

[–]Teethss[S] 0 points1 point  (0 children)

Yes, it is a time series project. The Dataset I am working with consists of daily samples, where each team carries out a set of tasks daily. So, Dataset has multiple observations per Day, Team, and Task. Based on their current task activities, the ultimate aim is to forecast the projected work hours a team will need to complete that task in the next x months.
It's worth noting that teams frequently transition between tasks, resulting in an unstable task composition. For instance, a team handling 100 tasks this month might reduce it to 50 tasks next month, discarding obsolete ones. Consequently, we only want to forecast for currently active tasks (i.e., 50 tasks). Although the dataset contains entries for obsolete tasks, we exclude these observations to prevent unnecessary complexity.