"If we can forecast the weather, and we know weather affects health, we can forecast how people's health will change over the next 10 days.
If we can forecast, then surely we can remember in the span of several minutes whether or not our forecast is correct based on our knowledge of the story.
If we cannot forecast the future, at least we have the past as a guide that should wisen us, alert us to the dangers of using that rhetoric".
If we can forecast future earthquakes, or tell in advance where, when and how big they will be, it would benefit to reduce damage from earthquakes and tsunamis.
So, from there, we just simply say that if we could forecast earnings perfectly, then the inverse of the P/E, or the earnings yield compared to interest rates four, five, seven years out, would be which again, we can't forecast with any perfection at all but if we could forecast that perfectly, we'd just make that forecast, we'd compare it to interest rate, which is clearly higher.
So if we want to forecast these days, we have the problem that we can never capture the public holiday effects.
On the other hand, if we model and forecast the subsequences of the system load and then sum them up to obtain the system load forecasting result (called the data-driven method in this paper), the modelling error for each subsequence will be more significant.
If we model and forecast the system load directly (called the direct method in this paper), the modelling error will be small because the load level y t is high and the standard deviation of the noise ɛ t is low due to the smoothness of the system load curve.
And the pension gap will widen if, as I forecast, we enter an era of mild deflation, low interest rates and depressed equity returns.
We suggest that, if forecast information is widely disseminated and adopted in the future, appropriate market or policy interventions may need to accompany the information to optimize societal benefit of climate forecasts.
