I'm doing some predictions on a time series dataset and have stumbled upon what I perceive as several variables being collinear. However, does time series accept multicollinearity as a possible property or is it concerned only with autocorrelation? The first plot shows the correlation matrix for the whole dataset while the second one shows the autocorrelation for the response variable. The third is the partial autocorrelation plot:
Looking at the second plot, one would say the periodicity of the distribution is regular even though I'm not getting correct predictions. Should I be caring about multicollinearity in time series data?
Let me add some more details about the data I have. Specifically, this is a traffic congestion time series that originally had 14 variables plus a 15th one which is the response variable:
print(main_data.columns) Index(['air_pollution_index', 'clouds_all', 'humidity', 'temperature', 'wind_direction', 'wind_speed', 'is_holiday_0', 'is_holiday_1', 'is_holiday_2', 'is_holiday_3', 'is_holiday_4', 'is_holiday_5', 'is_holiday_6', 'is_holiday_7', 'is_holiday_8', 'is_holiday_9', 'is_holiday_10', 'is_holiday_11', 'weather_descr_0', 'weather_descr_1', 'weather_descr_2', 'weather_descr_3', 'weather_descr_4', 'weather_descr_5', 'weather_descr_6', 'weather_descr_7', 'weather_descr_8', 'weather_descr_9', 'weather_descr_10', 'weather_descr_11', 'weather_descr_12', 'weather_descr_13', 'weather_descr_14', 'weather_descr_15', 'weather_descr_16', 'weather_descr_17', 'weather_descr_18', 'weather_descr_19', 'weather_descr_20', 'weather_descr_21', 'weather_descr_22', 'weather_descr_23', 'weather_type_0', 'weather_type_1', 'weather_type_2', 'weather_type_3', 'weather_type_4', 'weather_type_5', 'weather_type_6', 'weather_type_7', 'weather_type_8', 'weather_type_9', 'weather_type_10', 'dew_point_1', 'dew_point_2', 'dew_point_3', 'dew_point_4', 'dew_point_5', 'dew_point_6', 'dew_point_7', 'dew_point_8', 'dew_point_9', 'is_weekend', 'traffic_volume'], dtype='object')
traffic_volume is the target that I need to predict for a period from 2017 to 2018 on a daily basis. The rest of the features were engineered by me and are mostly categorical variables one hot encoded. Unfortunately I cannot plot the daily values for the response since that plot is tedious but I have decomposed it to make it more readable.