Deep Learning for Time Series in 2025

The Time Series Revolution

Time series forecasting has been transformed by deep learning. 2025 models consistently outperform ARIMA, ETS, and other statistical methods across domains.

Leading Architectures

Informer (2025 Update)

The state-of-the-art for long-sequence forecasting:

• ProbSparse attention: O(L log L) instead of O(L²)
• Distilling operations: Halve sequence length each layer
• Generative style decoder: One forward pass for all predictions

PatchTST

Simple but surprisingly effective:

• Divide time series into patches
• Apply standard transformer to patches
• Channel-independence (each series separate)
• Outperforms complex specialized architectures

import torch
from patchtst import PatchTST

model = PatchTST(
    input_length=512,
    output_length=96,
    patch_length=16,
    patch_stride=8,
    d_model=128,
    n_heads=8,
    n_layers=3
)

forecast = model(historical_data)

Temporal Convolutional Networks (TCNs)

When you need speed and simplicity:

• Dilated convolutions for long receptive fields
• Parallel processing (unlike RNNs)
• Linear time complexity
• Great for high-frequency data

Probabilistic Forecasting

Modern models output full distributions, not just point forecasts:

• Normalizing flows for complex distributions
• Quantile regression for prediction intervals
• Ensemble methods combining multiple models

Performance Benchmarks

| Dataset     | ARIMA | Prophet | Informer | PatchTST |
|-------------|-------|---------|----------|----------|
| Electricity | 0.32  | 0.28    | 0.18     | 0.16     |
| Traffic     | 0.45  | 0.41    | 0.28     | 0.25     |
| Weather     | 0.28  | 0.24    | 0.21     | 0.19     |
| Exchange    | 0.12  | 0.11    | 0.08     | 0.07     |
*Metrics: Mean Squared Error (lower is better)

Applications in 2025

• Finance: Algorithmic trading, risk forecasting
• Energy: Load prediction, renewable generation
• Retail: Demand forecasting, inventory optimization
• Healthcare: Patient monitoring, epidemic prediction

Frameworks

• GluonTS: Amazon's deep learning toolkit
• Darts: User-friendly with many models
• Nixtla: Production-focused time series