DarekarA / TimeSeriesForecasting

Agenda : • Time Series • Importance of Time Series • Smoothing Methods • Project on Time Series with Airlines dataset • ARIMA Model

Time Series :

1. It is an sequence of values of a variable at equal spaced time internal .
2. It is a series of datapoints ordered in time .
3. Time series analysis is a statistical technique that deals with time series data ,or trend aalysis.Time series data means that data is in a series of particular periods or intervals.
4. We refer today as T ,yesterday as T-1 day before yesterday as T-2 and henceforth .And T+1 for tomorrow and so on .
5. The data is considered in 3 types:
6. Time series data : A set of observation on the values that a variable take different times .
7. Cross-Sectional data: Data of one or more variables, collected at the same point in tie
8. Pooled data: A combination of time series data and cross-sectional data .
9. We dont take in consideration seasonal data or stats in time series .
10. We drop trends also in time series because it get vulnerabilities .
11. After dropping these factors we can do time series forecasting .
12. We can have different types of time interval .
13. Here have only 2 variable time and value. Importance of Time Series :
14. Time series is very important to solve a lot of problems in the business.
15. Based on time we create a lot of data .
16. We can use this to predict future operations. Application :
17. Economic Forecasting
18. Sales Forecasting
19. Budget analysis
20. Stock market analysis
21. Inventory studies etc

Time series components:

1. It can be described in terms of 4 basic classes of components:
2. Trend : It is a long term direction of a time series .It exist in long term increase or decrease in the data. It does not have to be linear, sometimes we will refer to a trend “changing direction”, when it might go from an increasing trend to a decreasing trend.
3. Seasonal : It is a regular pattern of variability within certain time periods such as year.
4. Cyclical: Any regular pattern of sequences of values above and below the trend.
5. Irregular: Cannot be defined it can change without a pattern.

Stationarity :

Rule for TimeSeries Data

• Data should be stationary..
• By nature no data has stationary format, We have to convert the data into stationary data then we can apply TimeSeries techniques.

Smoothing Methods:

1. It removes the random variations and shows trend and cyclic components.
2. When a time series contains a large amount of noise, it can be difficult to visualize any underlying trend.
3. There are 2 methods. ARIMA Model:
4. Auto Regressive (AR) Integrated(I) Moving Average (MA) ACF &PACF :
5. Auto Correlation Factor and Partial Auto Correlation Factor