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Experience Time Series Analysis and Forecasting Methods

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Experience Time Series Analysis and Forecasting Methods

In the first post of a series on time series, get an introduction to descriptive analysis, correlation analysis, and time series segmentation,

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Sometimes, words can motivate us and keep us moving in spite of failures. Celebrating birthdays and counting age will not help us have a successful professional life. So, along with time, we need some other attributes that are crucial to success. So, what is necessary?

First, the experience. Experience on the subject is a critical factor. Experience grows along with time, so we can consider experience as a function of time. The more time we spend on doing something or learning something, the more we will master the art of doing that task.

On the other hand, sometimes, experience is just not enough. Several other factors like employer's preference, location, work schedule, etc. also play a crucial role. With the right experience and right preference, we forecast the available opportunities and plan our action to give a good return on the time that we have put in to master the skill.

This analogy is valid for organizations as well. We can find a considerable amount of data in an organization where the temporal component (time) plays a vital role in the decision-making process; for example, sales and demand forecasts, customer churn, employee retention... the list goes on. Let's consider an example of the sales forecast. We can approach the problem in two different ways:

  1. Forecast only sales over time. We can call this univariate forecasting, as just a single attribute is considered along with time for the forecasting activity.

  2. We can also perform multivariate forecasting, where we consider multiple attributes such as product attributes, customer profiles, environmental factors, etc. along with sales for forecasting.

Understanding the different methods to work with and handle time series data is crucial. In this series of time series forecasting and analysis articles, we will explore various techniques to analyze and forecast time series data.

We have planned a series of articles that will focus on the below topics:

  • Exploring a time series in R.

  • Moving average and exponential smoothing methods for forecasting.

  • ARIMA modeling.

  • Dynamic regression model (ARIMAX).

Before we deep dive into time series forecasting, let's decipher what a time series is and how we can analyze it.

Time Series Analysis

A time series is a sequence of data points over equally spaced time intervals. The time order is important, and if the data is collected at irregular time intervals, it may not be considered as a time series. However, sometimes, some data points might be missed while capturing the data, i.e. if there's a communication problem between the IoT device and central data server. For such cases, we can consider imputing the data. There are methods like:

  • Replacing it with the overall mean value of the time series.

  • Replacing it with the variant of moving averages such as simple moving average, weighted, and exponential moving average.

  • Replacing the missing values with the most recent value before it.

You can try out imputeTS, an R package that provides several methods for imputing time series data.

Analyzing time series, in general, falls into one of the below categories:

  • Descriptive analysis: This includes visual inspection of time series data; decomposing the series to understand trends, seasonal patterns, outliers; and whether a series is stationary (constant mean and variance).

  • Correlation analysis: This includes examining the lagged correlation of various values in a time series, which is widely known as autocorrelation analysis. Also, we can analyze the relationship between two time series. For example, we can determine the correlation between the sales of two different geographical regions to determine whether there is any similarity between the two different sales patterns.

  • Time series segmentation: This is a method in which an input time series is divided into a sequence of discrete segments to extract information from extensive time series data points.

In the following part of the series, we will use R to examine and analyze time series data. 

Learn how taking a DataOps approach will help you speed up processes and increase data quality by providing streamlined analytics pipelines via automation and testing. Learn More.

time series ,data analytics ,big data ,tutorial

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