Embodiments of the invention provide a method and system for machine failure prediction. The method comprises: identifying a plurality of basic memory depth values based on a composite sequence of machine failure history; ascertaining weight values for at least one of the identified basic memory depth values according to a pre-stored table which includes a plurality of mappings wherein each mapping relates a basic memory depth value to one set of weight values; and predicting a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the ascertained weight values, wherein weight values related to a first basic memory depth value in the pre-stored table is ascertained based on a second set of weight values related to a second basic memory depth value which is less than the first basic memory depth value by a predetermined value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for machine failure prediction, the method comprising: identifying, by a first processor in a first computer system, a plurality of basic memory depth values based on a composite sequence of a machine failure history; ascertaining, by the first processor, a set of weight values for at least one of the plurality of basic memory depth values according to a pre-stored table which is stored in a memory and includes a plurality of mappings wherein each mapping relates a basic memory depth value to one set of weight values; and predicting, by the first processor, a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the ascertained weight values for the at least one of the plurality of basic memory depth values, wherein a first set of weight values related to a first basic memory depth value in the pre-stored table is ascertained based on a second set of weight values related to a second basic memory depth value, wherein the second basic memory depth value is less than the first basic memory depth value by a predetermined value. 2. The method according to claim 1, further comprising: determining, by the first processor, the number of state units to be used by the BPTT trained RNN based on a maximum value of the identified basic memory depth values. 3. The method according to claim 2, wherein the number of state units is further determined based on the following rules: if the maximum value of the identified basic memory depth values is not greater than 350, the number of state units is 60 units; if the maximum value of the identified basic memory depth values is more than 350 units, but not greater than 1000 units, the number of state units is 120 units. 4. The method according to claim 1, wherein the pre-stored table is generated according to the following steps: a. selecting, by a second processor in a second computer, an initial basic memory depth value and initial weight values; b. predicting, by the second processor, an elementary sequence corresponding to the initial basic memory depth value by running a RNN based on a predetermined error threshold, the selected initial basic memory depth value and initial weight values until convergence occurs; c. storing, by the second processor, a set of final weight values which are used for predicting the elementary sequence related to a current basic memory depth value when convergence occurs; d. increasing, by the second processor, the current basic memory depth value by the predetermined value if a predetermined maximum value of the basic memory depth value is not reached; e. predicting, by the second processor, an elementary sequence corresponding to the current basic memory depth value by running a RNN based on a predetermined error threshold, the current basic memory depth value and the set of final weight values which is most recently stored in step c, until convergence occurs; and f. repeating, by the second processor, the steps c to e until the predetermined maximum value of the basic memory depth value is reached. 5. The method according to claim 4, wherein in step b and/or step e, the elementary sequence is further predicted based on a predetermined minimum number of iterations for convergence. 6. The method according to claim 1, wherein the predetermined value is an integer not greater than 5. 7. The method according to claim 1, wherein the step of ascertaining a set of weight values for at least one of the plurality of basic memory depth values according to a pre-stored table, comprises: ascertaining a set of weight values for each of the plurality of basic memory depth values according to the pre-stored table; and the method further comprises: ascertaining a set of initial composite weight values by calculating weighted average of weight values ascertained for each of the plurality of basic memory depth values; and wherein the future failure is predicted based on the ascertained initial composite weight values. 8. The method according to claim 7, wherein the weighted average of weight values is calculated based on a count of appearances for each basic memory depth value in the composite sequence. 9. The method according to claim 1, wherein the composite sequence includes two basic memory depth values, wherein the step of ascertaining a set of weight values for at least one of the plurality of basic memory depth values according to a pre-stored table, comprises: ascertaining a set of weight values for a smaller value of the two basic memory depth values according to the pre-stored table; and the step of predicting a future failure, comprises: generating at least one intermediate composite sequence, wherein each intermediate composite sequence includes two basic memory depth values, one is the smaller value of the two basic memory depth values, the other one is an intermediate value between the two basic memory depth values; predicting the at least one intermediate composite sequence one by one according to an ascending order of the corresponding intermediate value of the at least one intermediate composite sequence, by running the BPTT trained RNN based on the set of weight values ascertained for the smaller value, or final weight values which are used for predicting an preceding intermediate composite sequence, until convergence occurs; and predicting the composite sequence by running the BPTT trained RNN based on final weight values which are used for predicting a last one of the at least one intermediate composite sequence, until convergence occurs. 10. The method according to claim 4, wherein the first processor and the second processor are provided as the same processor. 11. A system for machine failure prediction, the system comprising: a first processor and a memory communicably coupled thereto, wherein the first memory is configured to store data to be executed by the first processor, wherein the first processor is configured to identify a plurality of basic memory depth values based on a machine failure history; ascertain a set of weight values for at least one of the plurality of basic memory depth values according to a pre-stored table which is stored in a memory and includes a plurality of mappings wherein each mapping relates a basic memory depth value to one set of weight values; and predict a future failure using a Back Propagation Through Time (BPTT) trained Recurrent Neural Network (RNN) based on the ascertained weight values for the at least one of the plurality of basic memory depth values, wherein a first set of weight values related to a first basic memory depth value in the pre-stored table is ascertained based on a second set of weight values related to a second basic memory depth value, wherein the second basic memory depth value is less than the first basic memory depth value by a predetermined value. 12. The system according to claim 11, wherein the first processor is further configured to determine the number of state units be used by the BPTT trained RNN based on a maximum value of the identified basic memory depth values. 13. The system according to claim 12, wherein the first processor is further configured to determine the number of state units based on the following rules: if the maximum value of the identified basic memory depth values is not greater than 350, the number of state units is 60 units; if the maximum value of the identified basic memory depth values is more than 350 units, but not greater than 1000 units, the number of state units is 120 units. 14. The system according to claim 11, further comprising: a second processor in a second computer, wherein the second processor is configured to generate the pre-stored table according to the following steps: a. select an initial basic memory depth value and initial weight values; b. predict an elementary sequence corresponding to the initial basic memory depth value by running a RNN based on a predetermined error threshold, the selected initial basic memory depth value and initial weight values until convergence occurs; c. store a set of final weight values which are used for predicting the elementary sequence related to a current basic memory depth value when convergence occurs; d. increase the current basic memory depth value by the predetermined value if a predetermined maximum value of the basic memory depth value is not reached; e. predict an elementary sequence corresponding to the current basic memory depth value by running a RNN based on a predetermined error threshold, the current basic memory depth value and the set of final weight values which is most recently stored in step c, until convergence occurs; and f. repeat the steps c to e until the predetermined maximum value of the basic memory depth value is reached. 15. The system according to claim 14, wherein the second processor is further configured to predict the elementary sequence based on a predetermined minimum number of iterative for convergence. 16. The system according to claim 11, wherein the predetermined value is an integer not greater than 5. 17. The system according to claim 11, wherein the first processor is further configured to ascertain a set of weight values for each of the plurality of basic memory depth values according to the pre-stored table, ascertain a set of initial composite weight values by calculating weighted average of weight values ascertained for each of the plurality of basic memory depth values; and predict the future failure based on the ascertained initial composite weight values. 18. The system according to claim 17, wherein the first processor is further configured to calculate the weighted average of the weight values based on a count of appearances for each basic memory depth value in the composite sequence. 19. The system according to claim 11, wherein the composite sequence includes two basic memory depth values, wherein the first processor is further configured to ascertain a set of weight values for a smaller value of the two basic memory depth values according to the pre-stored table; generate at least one intermediate composite sequence, wherein each intermediate composite sequence includes two basic memory depth values, one is the smaller value of the two basic memory depth values, the other one is an intermediate value between the two basic memory depth values; predict the at least one intermediate composite sequence one by one according to an ascending order of the corresponding intermediate value of the at least one intermediate composite sequence, by running the BPTT trained RNN based on the set of weight values ascertained for the smaller value, or final weight values which are used for predicting an preceding intermediate composite sequence, until convergence occurs; and predict the composite sequence by running the BPTT trained RNN based on final weight values which are used for predicting a last one of the at least one intermediate composite sequence, until convergence occurs. 20. The system according to claim 14, wherein the first processor and the second processor are provided as the same processor. 21. A non-transitory computer readable medium comprising computer program code for machine failure prediction, wherein the computer program code, when executed, is configured to cause a processor in a computer system perform a method according to claim 1.