Research map / Time series / Long-term forecasting
Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract)
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Proposes a linear-space imagification method for multivariate time series (PCC-based re-stacking with sliding window plus two sin/cos time-embedding channels) to speed up training of a simple CNN forecaster on constrained devices.
Problem
Existing time-series imagification methods (RP, GASF, GADF) have quadratic O(n^2) space complexity, making CNN-based forecasting training impractical on resource-constrained IoT devices.
Approach
Re-stack multivariate series by Pearson correlation around the target series, slide a window to form the first channel, and add two sin/cos time-index channels that encode cyclic real-time information, yielding a 3-channel linear-size image.
Key results
- Proposed imagification reaches minimum error within 25 seconds versus more than 76 seconds for the other models (over 130 seconds in the worst case).
- Test MAE of 7.96 for the proposed method versus 9.37 (RP), 8.85 (GASF), 8.52 (GADF), and 8.87 (LSTM).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use proposed imagification instead of RP | CNN · Seoul highway traffic speed test dataset | MAE: 9.37 -> 7.96 | Helped |
| Use proposed imagification instead of GASF | CNN · Seoul highway traffic speed test dataset | MAE: 8.85 -> 7.96 | Helped |
| Use proposed imagification instead of GADF | CNN · Seoul highway traffic speed test dataset | MAE: 8.52 -> 7.96 | Helped |
| Use proposed imagification instead of LSTM | Seoul highway traffic speed forecasting · Seoul highway traffic speed test dataset | MAE: 8.87 -> 7.96 | Helped |
| Use proposed imagification with CNN instead of the compared models | CNN-based forecasting model · Seoul highway traffic speed forecasting | time to minimum error: within 25 seconds; other models required more than 76 seconds | Helped |
Limitations
- Evaluated only on a single traffic speed dataset with a simple CNN and one LSTM baseline.
- Reported accuracy differences are small and from a single run; no variance reported.
- Hardware and training details are not specified.
Experimental setup
Datasets: Seoul highway traffic speed data (105 road sections, hourly)
Benchmarks: Seoul highway traffic speed test set
Metrics: MAE training time to minimum error
Baselines: Recurrence Plots (RP) GASF GADF LSTM
Builds on: Recurrence Plots Gramian Angular Field
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