Part 1: Basic Introduction About Time-Series Forecasting

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Photo by: Kevin Ku |


Time Series Forecasting is the use of the statistical model to predict future value based on past observation/results. Use any variables tracked and collected over time. For example, annual population, daily stock prices, daily-weekly-quarterly sales, etc.

Time Series Data Characteristics & Objectives:

  • The data completely depend on data and time. Hence, if the order of date or time change leads to a change in the meaning of data.

Time Series Components

1. Time Series Plot

In Time-Series plot always, X-axis indicates Date/Time and Y-axis indicates Target Variable. For example, Want to predict monthly sales of the shop. Then X-axis: Days and Y-axis: Per day sales value.

2. Types of Trend in Time-Series plot

There are 3 types of trend can be observed in Time-Series graph. Also, each observed trend in Time-Series called Trend Cycle.

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Type of Trend | Image by:
  • Uptrend: The trend having HH (Higher High) & HL (Higher Low).

3. Seasonality

  • Time-Series exhibits a repeated pattern at a fixed interval of time. For example, Sales of Air-Conditioner increase during summers and decrease during winters.

3. Cyclical Pattern

  • Time-Series has no fixed period for rising/fall pattern.

Types of Time-Series Predictive/Forecasting Models

  1. ETS — Error Trend Seasonality Model

ETS models are non-stationary. ETS models are considered as exponential smoothing and then state-space. ETS model describes how unobserved components of the data (error, trend, and seasonality) change over time.

In the ETS model giving more weight to most recent observations or values with weights gradually gets smaller as observations get older.

There are 4 types of ETS methods:

  • Simple Exponential Smoothing Method (SES)

2. ARIMA — Auto-Regressive Integrated Moving Average Model

ARIMA models are stationary. ARIMA models focus on autocorrelation in data and forecast future trends.

There are two types of ARIMA models i.e. non-seasonal and seasonal.

Non-Seasonal ARIMA:


Where: p = Auto regressive value,

d = Number of transformation done to make data stationery,

m = Moving Average

Seasonal ARIMA:

ARIMA (p,d,m) (P,D,M) m


p: Autoregressive value,

d: Number of transformation done to make data stationery,

m: Moving Average

P, D, M: Autoregressive, number of transformation done to make data stationery, Moving Average of Seasonal Data.

m: Number of periods in each season


In this blog, I try to summarise the Time-Series its characteristics, types and about forecasting model. In upcoming blogs, I will explain each Time-Series Forecasting models (ETS & ARIMA) in details with Implementation using python.

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Data - Scientist | Analyst | Engineer | Enthusiast | ML Engineer

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