As　an　extensively　used　model　for　time　series　prediction,　the　Long–Short　Term　Memory　(LSTM)　neural　network　suffers　from　shortcomings　such　as　high　computational　cost　and　large　memory　requirement,　due　to　its　complex　structure.　To　address　these　problems,　a　PLS-based　pruning　algorithm　is　hereby　proposed　for　a　simplified　LSTM　(PSLSTM).　First,　a　hybrid　strategy　is　designed　to　simplify　the　internal　structure　of　LSTM,　which　combines　the　structure　simplification　and　parameter　reduction　for　gates.　Second,　partial　least　squares　(PLS)　regression　coefficients　are　used　as　the　metric　to　evaluate　the　importance　of　the　memory　blocks,　and　the　redundant　hidden　layer　size　is　pruned　by　merging　unimportant　blocks　with　their　most　correlated　ones.　The　Backpropagation　Through　Time　(BPTT)　algorithm　is　utilized　as　the　learning　algorithm　to　update　the　network　parameters.　Finally,　several　benchmark　and　practical　datasets　for　time　series　prediction　are　used　to　evaluate　the　performance　of　the　proposed　PSLSTM.　The　experimental　results　demonstrate　that　the　PLS-based　pruning　algorithm　can　achieve　the　trade-off　between　a　good　generalization　ability　and　a　compact　network　structure.　The　computational　complexity　is　improved　by　the　simple　internal　structure　as　well　as　the　compact　hidden　layer　size,　without　sacrificing　prediction　accuracy.　©　2022　Elsevier　B.V.