ChatLog
tsfresh
| ChatLog | tsfresh | |
|---|---|---|
| 1 | 4 | |
| 93 | 8,132 | |
| - | 1.0% | |
| 5.9 | 5.4 | |
| 28 days ago | 29 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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ChatLog
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ChatLog: Recording and Analyzing ChatGPT Across Time
While there are abundant researches about evaluating ChatGPT on natural language understanding and generation tasks, few studies have investigated how ChatGPT's behavior changes over time. In this paper, we collect a coarse-to-fine temporal dataset called ChatLog, consisting of two parts that update monthly and daily: ChatLog-Monthly is a dataset of 38,730 question-answer pairs collected every month including questions from both the reasoning and classification tasks. ChatLog-Daily, on the other hand, consists of ChatGPT's responses to 1000 identical questions for long-form generation every day. We conduct comprehensive automatic and human evaluation to provide the evidence for the existence of ChatGPT evolving patterns. We further analyze the unchanged characteristics of ChatGPT over time by extracting its knowledge and linguistic features. We find some stable features to improve the robustness of a RoBERTa-based detector on new versions of ChatGPT. We will continuously maintain our project at https://github.com/THU-KEG/ChatLog.
tsfresh
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For deep learning practitioners in industry, is the workflow always this annoying? [D]
This is definitely a good thing to try for time-series; you can automate your feature extraction too (eg using https://github.com/blue-yonder/tsfresh ).
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[D] Incorporating external data in LSTM models for sales forecasting in e-commerce
don't forget your feature engineering -> https://github.com/blue-yonder/tsfresh
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[R] Approach to identify clusters on a time series
Rather than the exact clustering algorithm, I think the main issue here is the feature extraction for the clustering. https://github.com/blue-yonder/tsfresh might be useful for that.
- Automatic time series feature extraction based on scalable hypothesis tests
What are some alternatives?
Auto_TS - Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.
tsflex - Flexible time series feature extraction & processing
Deep-Learning-Machine-Learning-Stock - Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and traders. [Moved to: https://github.com/LastAncientOne/Deep_Learning_Machine_Learning_Stock]
TimeSynth - A Multipurpose Library for Synthetic Time Series Generation in Python
deltapy - DeltaPy - Tabular Data Augmentation (by @firmai)
Deep_Learning_Machine_Learning_Stock - Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.
SDV - Synthetic data generation for tabular data
Time-Series-Transformer - A data preprocessing package for time series data. Design for machine learning and deep learning.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
tsfel - An intuitive library to extract features from time series.
Anomaly_Detection_Tuto - Anomaly detection tutorial on univariate time series with an auto-encoder