Timeseries datasets should first be representative enough of the business process they are dealing with.
For instance, some processes are better represented day by day, others, hours by hours, etc.
That's why you should first know the objective that will define the neccessary frequency and features, you cannot take all features and time values and get a good result: you should define at least one objective first, analyse your data, define which features seems meaningful, apply correlations functions to know the links between features, etc.
Once you have a good understanding of your dataset, you can start with a few features to see if your model work.
In your case, the best is to apply both solutions separately:
- Times series to detect how the market is changing and predict prices according to a few time-based features.
- Multi-dimensional prediction on most recent data in order to predict prices (or other things) using different features.
But you cannot have both solutions at the same time for a beginning. It is better to dissociate them using a few representative features, in order to get good results, and then increase complexity by crossing many feautures.