Time Series Analysis and Forecasting with Deep Learning

Introduction to Time Series Analysis


Time-Series refers to data recording at regular intervals of time. Time-Series components involve –


  • Trend – It increases, decreases or remains at a constant level with respect to time.
  • Seasonality – It is the property of Time -Series to display periodical patterns which repeats at a constant frequency.
  • Cycles – They do not repeat at regular time intervals and occur even if the frequency is 1.


Challenges to Time Series Forecasting


  • Sensor Data or Stock Market data consists of ~250 variables ‘col1’,’col2’…etc. and the data size is 15 gb.
  • Besides this, there are two critical categorical variables named ‘symbol,’ ‘categ’ and another variable ‘time.’
  • There are 22 unique symbols, and seven unique cats’s in the whole dataset recorded for every minute.
  • The target is to forecast ten future values of a column named ‘val’ for each symbol-categ pair.


Solution Offered for Time Series  Forecasting


Approaches for Time Series Analysis


Approach 1 – Convert Time Series Problem to Supervised Learning Problem.


  • Convert Time-Series data to Supervised Learning data.
  • Supervised Learning requires the values for all the independent variables.


Approach 2 – Using VAR(Vector Autoregression) Model.


  • It is an extension of the one dimensional Autoregressive Method. The advantage of this method is, no need to convert the Time Series Data to Supervised Learning Data.
  • Get the predictions for future values from the model itself. The model considers the interdependencies in the data.
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