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Forecasting
Meet the Author : Mr. Bharani Kumar
Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
Table of Content
Time Series vs Cross-Sectional Data
Time Series Data:
A crucial component of the data is data that has been gathered across intervals of time that are equally spaced apart.
Cross-sectional Data:
Data that can be collected at a single point of time.
Forecasting is the use of various modeling techniques to predict a future outcome on the basis of historical time series data.
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EDA - Components of Time Series
EDA in time series is mostly visual.
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Data Partition
Time series should be split in sequential order.
Most Recent period data will be chosen as Validation data.
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Conditions to choose the validation period:
- Forecast Horizon
- Seasonality
- Length of Time series
Forecasting Model
- Linear Regression
- Autoregressive models
- ARIMA
- Logistic regression
- Econometric models
- Naïve forecasts
- Smoothing
- Neural nets
Smoothing Techniques
Moving Average
- Centered Moving Average
- Trailing Moving Average
Exponential Smoothing
- Simple Exponential Smoothing
- Holt's Method/ Double Exponential Smoothing
- Winter’s Method
| Moving Average | Exponential Smoothing |
|---|---|
| Assigns equal weights to all past observations | Assigns more weight to recent observations than past observations |
| Better to forecast when data & environment is not volatile | Better to forecast when data & environment is volatile |
| Window width is key to success | Smoothing constant (α, β, γ) value is key to success (0 < α, β, γ ≤ 1) |
De-Trending and De-Seasoning
- To remove trend and/or seasonality, fit a regression model with trend and/or seasonality
- Series of forecast errors should be de-trended & deseasonalized
- Simple & popular for removing trend and / or seasonality from a time series
- Lag-1 difference: Yt – Yt-1 (For removing trend); Lag-M difference: Yt – Yt-M (For removing seasonality)
- Double differencing: difference the differenced series
- Uses moving average to remove seasonality
- Generates seasonal indexes as a byproduct
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