XenonStack Use Case Time Series Forecasting Analysis with Deep Learning

Introduction to Time Series Analysis

• 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

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

• 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.

Understanding Pattern Analysis and its Components

Components of Pattern Analysis

• Trend Analysis
• Seasonality Analysis

Trend Analysis Overview

Seasonality Analysis Overview