Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Time series prediction algorithms are widely used for applications such as demand forecasting, ather forecasting and many others to make ll informed decisions. ,  compare the most prevalent of these methods as ll as suggest our own, where the time series are generated from highly complex industrial processes. These time series are non-stationary and the relationships beten the various time series vary with time. Given a set of time series from an industrial process, the challenge is to keep predicting a chosen one as far ahead as possible, with the knowledge of the other time series at those instants in time. This scenario occurs, since the chosen time series is usually very expensive to measure or extremely difficult to obtain compared to the rest. Our studies on real data suggest, that our method is substantially more robust to predicting multiple steps ahead than the existing methods in these complex domains.
['Amit Dhurandhar']
I am writing a research paper on a idea Multi-step Time Series Prediction in Complex Instrumented Domains
[{"from": "human", "value": "I am writing a research paper on a idea Multi-step Time Series Prediction in Complex Instrumented Domains"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Time series prediction algorithms are widely used for applications such as demand forecasting, ather forecasting and many others to make ll informed decisions. ,  compare the most prevalent of these methods as ll as suggest our own, where the time series are generated from highly complex industrial processes. These time series are non-stationary and the relationships beten the various time series vary with time. Given a set of time series from an industrial process, the challenge is to keep predicting a chosen one as far ahead as possible, with the knowledge of the other time series at those instants in time. This scenario occurs, since the chosen time series is usually very expensive to measure or extremely difficult to obtain compared to the rest. Our studies on real data suggest, that our method is substantially more robust to predicting multiple steps ahead than the existing methods in these complex domains."}]