Time-series　forecasting　has　a　wide　range　of　application　scenarios.　Predicting　particulate　matter　with　a　diameter　of　2.5　mu　m　or　less　(PM2.5)　in　the　future　is　a　vital　type　of　time-series　forecasting　task　where　valid　forecasting　would　provide　an　important　reference　for　public　decisions.　The　current　state-of-the-art　general　time-series　model,　TimesNet,　has　achieved　a　level　of　performance　well　above　the　mainstream　level　on　most　benchmarks.　Attributing　this　success　to　an　ability　to　disentangle　intraperiod　and　interperiod　temporal　variations,　we　propose　TimesNet-PM2.5.　To　make　this　model　more　powerful　for　concrete　PM2.5　prediction　tasks,　task-oriented　improvements　to　its　structure　have　been　added　to　enhance　its　ability　to　predict　specific　time　spots　through　better　interpretability　and　meaningful　visualizations.　On　the　one　hand,　this　paper　rigorously　investigates　the　impact　of　various　meteorological　indicators　on　PM2.5　levels,　examining　their　primary　influencing　factors　from　both　local　and　global　perspectives.　On　the　other　hand,　using　visualization　techniques,　we　validate　the　capability　of　representation　learning　in　time-series　forecasting　and　performance　on　the　forecasting　task　of　the　TimesNet-PM2.5.　Experimentally,　TimesNet-PM2.5　demonstrates　an　improvement　over　the　original　TimesNet.　Specifically,　the　Mean　Squared　Error　(MSE)　improved　by　8.8%　for　1-h　forecasting　and　by　22.5%　for　24-h　forecasting.