Multivariate Time series
Most of the commonly used techniques are based on time series data (predict tomorrows value based on the values from yesterday, last week, last month, etc)
Since you have some features in your dataset that are not time series (feature_1), you may want to look at Causal Forecasting along with classical time series forecasting. One example of Causal Forecasting the Vector Auto Regressive model (VAR), combined with Granger Causality to look at the relationship over time between different time series and build a model to predict future events.
Definition of causal forecasting:
&quot;Causal forecasting is the technique that assumes that the variable to be forecast has a cause-effect relationship with one or more other independent variables. Causal techniques usually take into consideration all possible factors that can impact the dependent variable&quot;
reference:
https://www.brillio.com/insights/choosing-the-right-forecasting-technique/
other references:
causal:
https://towardsdatascience.com/granger-causality-and-vector-auto-regressive-model-for-time-series-forecasting-3226a64889a6
https://courses.edx.org/asset-v1:MITx+CTL.SC1x_1+2T2015+type@asset+block/w4l1_causalmodels_ANNOTATED_FINAL.pdf
classical:
https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/