To　tackle　dynamic　multiobjective　optimization　problems　(DMOPs),　researchers　have　developed　many　prediction　approaches　based　on　dynamic　multiobjective　evolutionary　algorithms　(DMOEAs).　Howecer,　most　methods　only　predict　in　a　single　decision　or　objective　space.　Most　existing　prediction　methods　are　generally　utilized　the　historical　Pareto　optimal　solutions　(POS)　obtained　in　the　decision　space　to　predict　the　initial　population.　However,　changes　in　DMOP　may　happen　simultaneously　in　both　decision　and　objective　spaces.　Therefore,　predicting　only　in　decision　space　may　not　provide　accurate　estimates　of　changes　in　DMOP.　In　response　to　this　issue,　a　two-space　based　prediction　strategy　(TSP)　is　proposed,　which　is　adapted　to　environmental　changes.　First,　a　denoising　autoencoder　search　model　is　built,　which　is　predicted　from　the　perspective　of　decision　space.　Then,　directly　predict　individuals　in　the　objective　space.　After　that,　an　improved　inverse　model　is　designed,　which　is　to　map　the　predicted　individuals　to　the　decision　space.　Finally,　new　solutions　predicted　based　on　decision　and　objective　spaces　are　gathered　together　to　form　the　initial　population.　The　experimental　results　display　that　TSP　is　effective　in　handling　most　DMOP　test　cases.　©　2023　IEEE.