## 1 Introduction  
Day-ahead electricity market (DAM) prices have historically been driven by fundamental drivers or fundamentals reflecting an intrinsic demand and supply of electricity. Fundamentals are associated with the intrinsic value of a good, commodity, or security. Examples include goods' production costs, economic variables, etc. In a world of perfectly rational investors, only fundamentals are considered to drive prices as investors always optimally maximise their utility. In recent years, regulatory changes, such as [1], have swept through electricity markets provoking disruptive propagator shocks. These shocks, which culminated from a growing need to generate cleaner and safer energy [2, 3], have indirectly boosted the impacts of technical price drivers or technicals on the DAM and moved prices further from their intrinsic values. The term \"technical\" comes from the discipline of technical analysis (TA) [4]. Technicals are factors which move prices away from their intrinsic values. They result because, in practice, investors are not perfectly rational and often make seemingly sub-optimal decisions. Behavioural economics focuses on explaining how investors' biases, emotions, and other psychological factors influence decisions and, by extension,prices. As a result of the impacts of technicals, the identification of new model features, capable of capturing the residual impacts of technicals, has become necessary to forecast DAM prices. Describing the propagation of regulatory shocks across day-ahead and intraday markets, the incentivisation of renewable electricity generation spurred investment in renewable technologies, increasing efficiencies, reducing costs, and accelerating uptake. An accelerating uptake of renewables, however, increased electricity demand and supply variability and, by extension, volume and price forecast uncertainty. Given the need to avoid energy imbalances, a greater uncertainty surrounding delivery volumes impacted traders' planning decisions, amplifying day-ahead and intraday trading. Amplified intraday trading, in turn, increased the market's liquidity and decreased its average bid-ask spread, provoking a feedback loop which spurred yet more intraday trading. Although DAM volumes continued to overwhelmingly exceed intraday volumes throughout, greater intraday liquidity further impacted traders' planning decisions; boosting their willingness to distribute orders across day-ahead and intraday markets. Because traders' decisions are not always perfectly rational, a greater willingness to distribute orders across day-ahead and intraday markets accentuated the impacts of technicals, moving prices away from their intrinsic values. Exemplifying this point, note that all trades on electricity markets are placed because of an underlying stochastic energy need. Whereas before the boom in renewable generation, traders almost exclusively utilised the DAM to meet this need in the short-term, resulting in planning decisions rooted principally in fundamentals, today, intraday opportunities offer traders many more ways to maximise their respective reward to risk ratio, resulting in more heterogeneous short-term planning decisions and the proliferation of technicals. Day-ahead and intraday substitution can thus be understood to have heightened the impacts of technicals on DAM prices. Introducing existing modelling approaches for forecasting DAM prices, historically researchers have relied upon features primarily reflecting the fundamentals of demand and supply. On the supply side, features, such as gas and coal prices [5], which capture the cost of generating electricity, have been used. On the demand-side, with weather conditions and circadian rhythm dictating consumers' daily routines, meteorological factors [5] as well as seasonal dummies [6], ranging from the hour of the day to the month of the year, have been used.