Accurate one-hour-ahead electricity load forecasting sustains dispatch, reserve planning, and dependable power-system operation, but additional inputs do not always improve predictions. This study assesses how different parameter settings contribute to power demand forecasting. Four long short-term models with a unified architecture were trained on 48048 hourly observations and a 24-hour window. Input configurations include historical demand alone, demand with twelve weather variables, demand with two calendar indicators, or all inputs. Each configuration was trained five times and evaluated on a chronological test set. The weather-based model achieved the lowest mean errors: a mean absolute percentage error of 1.599% and a root mean square error of 26.333. The history-only model remained competitive, while calendar indicators and the all-input model offered no improvement. Recent demand therefore provides most of the useful information at this horizon, with lagged weather adding a modest signal. Careful feature selection can reduce complexity and support interpretable, dependable operational forecasts.

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Short-term taxi order forecasting is a key reference for dynamic allocation of urban transportation capacity. New York taxi passenger flow exhibits significant intraday cyclical fluctuations, making it difficult for manual dispatch to predict changes in passenger flow. This paper takes hourly taxi order time-series data in New York City as the research object, selecting the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and the Support Vector Regression (SVR) model to conduct a short-term order prediction comparison. Based on AIC and BIC criteria, the optimal SARIMA parameters are traversed and screened, and SVR data are subjected to Min-Max normalization. Both types of models use multi-step rolling forecasting to generate a complete 7-day prediction sequence, relying on MAE, RMSE, MAPE, and R2 indicators to quantify prediction accuracy. Experimental results show that the optimal SARIMA (1,0,1)(1,1,24) model can accurately fit peak and valley fluctuations in passenger flow, with an R2 of 0.815. SVR models can only capture overall trends and lack the ability to characterize extreme passenger flows. The study demonstrates that for taxi time series data with strong intraday cycles, the SARIMA model offers better prediction stability and accuracy, providing a reference for taxi capacity scheduling.

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Accurate hourly bike-sharing demand prediction is important for bicycle rebalancing and resource allocation. This study evaluates the individual and combined contributions of environmental and temporal features to hourly bike-sharing demand prediction using 17,414 hourly records from the London Bike Sharing Dataset. A time-ordered 80:20 split is adopted to mimic forecasting future demand from historical observations. A distance-weighted k-nearest neighbors (KNN) regressor is adopted as the baseline, and a tuned random forest (RF) regressor serves as the main model. RF achieves MAE=173.08 rentals/h, RMSE=304.76 rentals/h, and R²=0.927, reducing MAE and RMSE by 65.7% and 60.2% compared with KNN. Feature-combination experiments show that environmental features alone have limited explanatory power (R²=0.132), whereas temporal features capture the main demand structure (R²=0.868). After feature fusion, MAE and RMSE decrease by 29.0% and 25.6% relative to the temporal-only setting. Visual analyses and permutation importance indicate that temporal variables establish the main demand baseline, while environmental variables provide complementary corrections. The results offer interpretable evidence for feature selection and short-term bike-sharing operations.

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Intrusion detection systems built on hand-written rules are losing ground as attackers find new ways to bypass fixed signatures. Working with the NSL-KDD benchmark, the paper put three classical machine learning methods—Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbors (KNN)—through the same set of binary and five-class detection tests. Random forest handled the diverse five-class label set best, hitting 76.3% accuracy with an F1 of 0.720. In the simpler binary task, however, the decision tree pulled ahead (81.2%, F1 0.813). The reason was straightforward: the ensemble model grew too cautious when samples were scarce, suppressing false alarms at the cost of missing real attacks. DoS floods were caught almost without fail (F1 0.884). R2L and U2R, on the other hand, evaded detection almost entirely—their training presence was vanishingly small, at 0.83% and 0.04% of the training pool. Feature importance scores from the random forest pointed overwhelmingly to traffic volume and rate-based statistics as the strongest signals, while class skew stood out as the single largest obstacle. Rather than chasing marginal accuracy gains, the results argue for treating rare-class detection as the main priority in future work.

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