I'm new for data analysis. I got some data from the regional environmental center.
Measurements: Datetime, PointID, SubstanceID, Value (substances concentrations in air), MeteoID ,NextValue
Meteorological data: MeteoID, Datetime, temperature, wind speed, wind diretion,humidity, pressure, precipitation.
Substances(8 substances(CO,Cl2,NO2, etc.), 5 of them have about 1.2 million records): SubstanceID, Name, MaxVal (maximum allowable concentration value).
Points(9 static monitoring stations): PointID, Adress, Longitude, Latitude.
Measurments table contains about 8 million records of substances concentratios in air(local mysql). I linked the measurement data and the meterologicala data (closest in time), and added the next value to measurement record. time between measurements 20 min( in average, for some periods data are missed).
I want to make a predictive data analysis and get short-term forecasts for substances concentrations depending on the previous value and weather data(may be for few hours). I am still thinking which methods, techniques can be chosen. I want to consider different methods. which toolkit is most suitable for this case? Should the data be considered as a multidimensional time series? I'm currently looking into the direction of some kind of neural network and implementation in python (but I still don’t know which package).