| # Time Series Datasets |
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| We are the Time Series Analysis Team of Southeast University. |
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| email: zysong@seu.edu.cn |
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| We are developing a new benchmark that includes a broader dataset and a more lightweight code framework to address the issue of excessive encapsulation in current time series forecasting libraries. |
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| - COMMON: such as ETT, Traffic, Electricity, PEMS |
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| - TFB: form paper "TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods" |
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| - WORKLOAD: form paper "Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services", ByteDance |
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| ## Dataset format |
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| ```python |
| dict: |
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| 'data': np.array, shape: (length, num_variates) |
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| 'time_date': the DatetimeIndex, shape: (length,) |
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| 'columns': np.array, shape: (num_variates,) |
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| 'freq': np.str |
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| 'cycle': np.array, the series cycles (*e.g.*, 24, 24*7 and so on). We don't test the cycle if the value is -1. |
| ``` |
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| ## Loading Dataset |
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| ```python |
| import numpy as np |
| import pandas as pd |
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| df_data_columns_date = np.load(file_path, allow_pickle=True).item() |
| df_data = df_data_columns_date["data"] # shape (seq_len, num_features) |
| # df_columns = df_data_columns_date["columns"] # list of column names |
| df_date = df_data_columns_date["time_date"] # pd.DatetimeIndex (seq_len, ) |
| ``` |
