After reaching PSSZ in the hybrid material, AzoGly back-isomerization was monitored under the same-length dark (OFF) and light (ON, red-NIR irradiation, 650-1100 nm, 145.8 mW cm -2 ) intervals. The collected spectra were accordingly processed (see Materials and methods in the SI) to conform with the assumptions of the applied statistical modelling approach -interrupted time series (Section 5.2. of the SI, Figure ). We consider observations before and after an intervention, which is the introduction of light. Intervention occurs a few times during each experiment (irradiation source was switched on and off multiple times). Dark and subsequent light intervals are paired up (in this exact order) and each pair is considered separately for a given sample. Our central hypothesis is that in the presence of Au, due to the plasmon-assisted Azo isomerization triggered upon irradiation, there are statistically significant differences within subsets (pairs) upon intervention (Figure ). Once thermoplasmonic effect is triggered and the collective heating of AuNRs causes temperature rise in the vicinity of the nanostrucutres, AzoGly as a T-type photochrome should undergo Z-E isomerization faster. Hence, the slope of the linearized data should increase in absolute value during light period (Figure ), which can be qualitatively assessed through naked eye inspection (Figure ). However, a non-trivial composition of our materials requires appropriate statistical tools to extract quantitative data. Auto-correlation of the observations violates the assumptions of classical linear models and to account for the temporal structure of the data we analyzed it with an Autoregressive Integrated Moving Average (ARIMA)based approach. ARIMA models represent a single variable of interest 𝑌 𝑡 as a function of its previous values (e.g. 𝑌 𝑡-1 ), random error (𝜖 𝑡 ), and previous values of the random error (e.g. 𝜖 𝑡-1 ), seasonal effect, differencing, and optional covariates (Figure ,e and Section 5.2. of the SI).