## 3 Results
### Monthly Performance Improvements  
In Section 3.1, we identified EMA (\(s=2\)), MOM (\(n=58\)), and ROC (\(n=49\)) as being among our best performing linear, ensemble, and deep model TIs. Table 2 emphasized that each of these TIs, however, impacted forecast accuracies differently. While EMA (\(s=2\)), for instance, significantly improved the accuracy of tail forecasts, it did little to improve the accuracy of IQR forecasts. ROC (\(n=49\)), on the other hand, improved both IQR and tail forecast accuracies. Below, in an attempt to identify any prevailing patterns in forecast improvements, we visualise and analyse TI performance across the entire test set. Despite performance being model dependent, we aim to identify a link between forecast improvement and the use-cases of each TI.  
Focusing on EMA (\(s=2\)), overall we found that this TI increased the accuracy of 74.65% monthly HR forecasts, as visualised in Figure 5. Examining the distribution of EMA forecast improvements in the figure, we observe that a majority of large forecast improvements occurred during peak hours: between 08-20h. Moreover, by overlaying Figures 4 and 5, we discern that these generally correspond to periods of above-average price and volatility. To potentially understand why the inclusion of EMA (\(s=2\)) features results in such improvements, consider the information short-term EMAs convey, as well as their use-case. Short-term EMAs alongside lagged prices are used to identify short-term trend formations, reversals, etc. Practitioners especially use short-term EMAs during periods of increased volatility. This use-case may potentially explain why EMA (\(s=2\)) increased peak-hour forecast accuracies the most.  
Turning to MOM (\(n=58\)), the MOM increased the accuracy of 64.24% of monthly AB forecasts. At a glance, Figure 5 improvements appear to be sporadically distributed; not clusterable by season or hour. Nevertheless, overlaying Figures 4 and 5 suggests that greater %RMSE improvement occurred during periods of average volatility, after prices had peaked or troughed. Outlining the potential reasons for this, recollect from Section 2.1.6 that the MOM, as an unbounded oscillating TI, is used to forecast emerging trends. Specifically, MOM/price divergence and zero line crosses are thought to indicate bullish or bearish signals. For the MOM to provide additional explanatory power, however, volatility must be near the normal range (IQR) to facilitate the formation of regular and sustained price fluctuations. This is because extreme volatility can generate whipsaws--i.e., a MOM curve that crosses the zero line too frequently, with sporadic regularity. Overall, this potentially explains why MOM (\(n=58\)) improved the accuracies of forecasts after prices had peaked or troughed, and after momentum had established longer-term trends.  
Finally considering ROC (\(n=49\)), we found that the ROC increased the accuracy of 71.53% monthly 2 CNN forecasts. Unlike the EMA with HR, however, performance improvements were not divided broadly between peak and off-peak hours. Instead, as overlaying Figures 4 and 5 highlights, the performance improved more significantly during periods of roughly normal (IQR) volatility as prices peaked or troughed. An understanding of the ROC's use-cases allows us to potentially explain the reasons behind this improvement. Specifically, recall from Section 2.1.7 that the ROC is principally used for the identification of overbought or oversold extremes that historically foreshadowed a trend reversal. Little information is captured by the ROC during periods of price consolidation and low volatility. Similarly, during periods of extreme volatility, when sporadic price changes ensue, previously informative overbought or oversold signals lose their predictive power. This potentially explains why ROC (\(n=49\)) added more explanatory power during periods of rising/falling prices when volatility was near its IQR.