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steps_to_evaluate_your_own_time_series.md

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Steps to evaluating your own time series

  1. Download the datasets from Google Drive.
  2. Put the downloaded data under the folder ./dataset.
  3. Process your dataset into three columns in TFB format using code like here.
  4. Put the processed dataset under: ./dataset/forecasting folder.
  5. Run the following shell commands to evaluate on your own datasets：
  • Evaluate on several user-defined forecasting datasets, **please set the "--data-name-list" parameter to "your own dataset name"；**If you want to evaluate more than one series, separate the dataset names with commas.
python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "your series name1" "your series name2" "your series name3" ...

A detailed example is provided below:

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-name-list "your series name1" "your series name2" "your series name3" --strategy-args '{"horizon":24}' --model-name "time_series_library.DLinear" --model-hyper-params '{"batch_size": 16, "d_ff": 512, "d_model": 256, "lr": 0.01, "horizon": 24, "seq_len": 104}' --adapter "transformer_adapter" --gpus 0 --num-workers 1 --timeout 60000 --save-path "ILI/DLinear"
  • Evaluate on all user-defined forecasting datasets, please set the "--data-set-namet" parameter to "user_forecast"; At this point, all the datasets you uploaded will be evaluated.
python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-set-name "user_forecast"

A detailed example is provided below:

python ./scripts/run_benchmark.py --config-path "rolling_forecast_config.json" --data-set-name "user_forecast" --strategy-args '{"horizon":24}' --model-name "time_series_library.DLinear" --model-hyper-params '{"batch_size": 16, "d_ff": 512, "d_model": 256, "lr": 0.01, "horizon": 24, "seq_len": 104}' --adapter "transformer_adapter" --gpus 0 --num-workers 1 --timeout 60000 --save-path "ILI/DLinear"

TFB data format

TFB stores time series in a format of three column long tables, which we will introduce below：

Format Introduction

First column: date (the exact column name is required, the same applies below.)

  • The columns stores the time information in the time series, which can be in either of the following formats:
    • Timestamps in string, datetime, or other types that are compatible with pd.to_datetime;
    • Integers starting from 1, e.g. 1, 2, 3, 4, 5, ...

Second column: data

  • This column stores the series values corresponding to the timestamps.

Third column: cols

  • This column stores the column name (variable name).

Multivariate time series example:

A common time series in wide table format:

date channel1 channel2 channel3
1 0.1 1 10
2 0.2 2 20
3 0.3 3 30

Convert to our format:

date data cols
1 0.1 channel1
2 0.2 channel1
3 0.3 channel1
1 1 channel2
2 2 channel2
3 3 channel2
1 10 channel3
2 20 channel3
3 30 channel3

Univariate time series example:

A common time series in wide table format:

date channel1
2012-09-26 12:00:00 0.1
2012-09-27 12:00:00 0.2
2012-09-28 12:00:00 0.3
2012-09-29 12:00:00 0.4
2012-09-30 12:00:00 0.5

Convert to our format:

date data cols
2012-09-26 12:00:00 0.1 channel1
2012-09-27 12:00:00 0.2 channel1
2012-09-28 12:00:00 0.3 channel1
2012-09-29 12:00:00 0.4 channel1
2012-09-30 12:00:00 0.5 channel1

Example code

import pandas as pd


def convert_to_tfb_series(data):
    data = data.set_index("date")
    melted_df = data.melt(value_name="data", var_name="cols", ignore_index=False)
    return melted_df.reset_index()[['date', 'data', 'cols']]

data = pd.read_csv(r"./ori-Exchange.csv")
melted_df = convert_to_tfb_series(data)
melted_df.to_csv(r"./converted-Exchange.csv", index=False)