In conventional Trend Impact Analysis (TIA), a baseline model based forecast is generated using historical data. Also, a set of future events and their impacts are identified utilizing prior knowledge. Further, these impacts and events are combined with baseline to generate possible future scenarios through simulation. One of the main drawback of this approach is that it cannot deal with unprecedented future technologies or rare events. Further, it cannot answer about expected future, if some specific event occurs at a particular period in future. Intervention analysis has been traditionally used to assess the impact of any unprecedented event occurring at known times on any time series. It consists of a single impact parameter and a slope parameter for a particular event. Hence, a new TIA method has been developed by combining conventional TIA with the intervention model instead of simulation, The traditional interventional model were modified as per the requirement of TIA to incorporate three impact parameters for any number of events. For the unprecedented future event, impact of the event is known while time at which event will occur is not known in advance. A formula for estimating slope parameter has been derived. The proposed TIA approach is capable to handle the influence of any unusual occurrences on the structure of the fitted model while providing forecasts of future values. The data requirements in this proposed new TIA is less as compared to conventional TIA approach. It can also answer about expected future if some particular event occur in particular time. The proposed TIA approach has been empirically illustrated for wheat yield scenario at All-India level. For this, three events each with three degrees of severity have been considered. All possible scenarios were generated from which preferable futures can be chosen.