# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Results
#### 5.3.2 Task-Specific Model Performance Analysis  
While overall performance metrics provide a high-level comparison of the models, they do not fully capture how each architecture performs on specific tasks. Accordingly, the next section explores the task-specific analysis of the model performance. Table 1 provides a detailed list of the 46 unique tasks covered in the surveyed papers. To facilitate meaningful comparisons, these tasks are grouped into 10 broader categories based on shared characteristics and application domains (Figure 7). \begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Time Series Prediction Task** & **Frequency** & **Group** \\ \hline
1 & Total Electricity Consumption (Demand) & 8 & 1 \\
2 & Load Forecasting & 2 & 1 \\
3 & Electricity Theft Prediction & 1 & 1 \\
4 & Heating Load Prediction & 1 & 1 \\
5 & Return Temperature of District Heating System & 1 & 1 \\
6 & Electricity Consumption of an Electric Bus & 1 & 1 \\
7 & Solar Power Forecasting & 3 & 2 \\
8 & Wind Power Forecasting & 2 & 2 \\
9 & Rainfall Prediction (Including Rainfall Runoff) & 2 & 2 \\
10 & Drought Prediction & 2 & 2 \\
11 & River Inflow Prediction (Including Reclaimed Water Volumes) & 2 & 2 \\
12 & Subsurface Temperature (Including Sea Surface Temperature) & 2 & 2 \\
13 & Reservoir Water Level Prediction & 1 & 2 \\
14 & Flood Frequency & 1 & 2 \\
15 & Groundwater Availability & 1 & 2 \\
16 & Indoor Daylight Illuminances Prediction & 1 & 2 \\
17 & Crop Yield (Including Corn Biomass, Crop Height) & 5 & 3 \\
18 & Crop Classification & 3 & 3 \\
19 & Water Quality Prediction (Including Chlorophyll-a and Wastewater Treatment) & 8 & 4 \\
20 & Air Quality & 1 & 4 \\
21 & Passenger Demand (Includes Bike Sharing, Urban Rail Passenger Flow) & 3 & 5 \\
22 & Travel Time Prediction & 1 & 5 \\
23 & Future Traffic of Mobile Base Stations in Urban Areas & 1 & 5 \\
24 & Traffic Queue Length & 1 & 5 \\
25 & Tunnel Deformation Prediction & 1 & 6 \\
26 & Dam Structural Health Prediction & 1 & 6 \\
27 & Highway Tunnel Pavement Performance & 1 & 6 \\
28 & Predict Temperature Trend of Wind Turbine Gearbox & 1 & 6 \\
29 & Discharge Capacity Estimation for Li-Ion Batteries & 1 & 6 \\
30 & Sintering Process Prediction & 1 & 6 \\
31 & Stock Price (Including Crypto/Stock Trend) & 3 & 7 \\
32 & Hedge Fund Return Prediction & 1 & 7 \\
33 & Store Item Demand & 1 & 7 \\ \hline \end{tabular}
\end{table} 
Table 1: Illustrates the 46 time series prediction tasks present in the surveyed papers. The frequency column shows the number of occurrences of the tasks, and the group column shows the assigned category number of each task. For each task category, the performance of TBML models and DL models is compared, with additional focus on the best-performing subclasses within these categories: RNNs and SPTB models. Figures 8 and 9 illustrate the comparative performance of these model classes using the FPA and WRA metrics, respectively. The results highlight that TBML models consistently outperform DL models in Task Groups 1, 5, 9, and 10. In these categories, TBML models demonstrate performance advantages ranging from 37.5% to 68.85% in WRA scores and 25% to 150% in FPA scores. Conversely, DL models show significant superiority in Task Groups 2, 6, and 7, with performance improvements of 28.57% to 97.26% in WRA and 50% to 500% in FPA compared to TBML models. A more nuanced comparison between RNN and SPTB models reveals that SPTB models perform notably better than RNNs in Task Groups 5 and 10, with WRA improvements of 45.83% and 22.22% and FPA improvements of 25% and 66.67%, respectively, compared to RNNs. In contrast, RNN models excel over SPTB models in Task Groups 2, 4, 6, 7, and 8, with performance gains compared to SPTB models ranging from 31.25% to 84.32% in WRA and 50% to 260% in FPA.