Data-Driven Component-Wise Forecasting of Heterogeneous Vehicle–Environment Temperature Channels for Infrared-Oriented Applications

Forecasting heterogeneous vehicle–environment temperatures requires models that accommodate differences in amplitude, persistence and meteorological response while keeping component-model proliferation manageable. This study presents a channel-adaptive component forecasting framework and directed reuse of frozen dominant-component predictors. RIME selects complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) parameters using decomposition quality. A common component predictor combines static feature weighting, dilated causal convolution, historical-window bidirectional long short-term memory encoding, temporal attention and an autoregressive highway (DConvFTA-BiLSTM-AR). A field campaign in Xuzhou, China, provides 14,217 consecutive one-minute records from 64 vehicle and environmental temperature channels. Fourteen channels spanning contrasting materials and thermal regimes were benchmarked using a chronological 70/20/10 split, train-fitted scaling and decomposition restricted to the train–validation prefix. Under observed future meteorological inputs, macro MAE was 0.264 °C and macro MSE was 0.430 °C2. The MAE was 13.7% lower than that of CEEMDAN-LSTM, the strongest neural baseline by macro MAE, in the evaluated interval. Directed reuse tests identify compatible source–recipient pairs while retaining recipient-specific predictors for non-dominant components and the residual. For 13 exposed-surface channels, a fixed-emissivity Planck transformation gives mean self-emitted 8–14 μm radiant-exitance MAE and channel RMSE of 0.593 and 1.280 W/m2. These results establish component forecasting and partial predictor reuse as useful tools for the evaluated heterogeneous thermal array and provide temperature inputs for downstream infrared analysis.