# Time Series Forecasting Analysis with Deep Learning

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## 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.

## Understanding Pattern Analysis and its Components

Pattern Analysis with Time Series Data involves nature identification represented by a sequence of observations and forecasting including prediction of future values Time- Series variable. Time Series Pattern Analysis consists of systematic pattern data called as a set of identifiable components and random noise error which makes pattern identification difficult.

### Components of Pattern Analysis

• Trend Analysis
• Seasonality Analysis

### Trend Analysis Overview

The trend is described as a linear function to eliminate non-linearity through a log or exponential functions. If an error occurs in trend, then smoothing is required such as a moving average with components replacement of the series with a simple or weighted average.

### Seasonality Analysis Overview

It consists of autocorrelation correlograms to display serial correlation for consecutive lags and examining correlograms, removal of serial dependencies and partial autocorrelations.

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