Unlocking the Secrets of Time Series Data

Time series analysis is a technique for examining and fully understanding historical data gathered over time. It is a useful tool for recognizing patterns and trends in data and making predictions about future events.

Image Reference: FlexTrade Systems

Time series data can be found in many fields, including finance, economics, biology, and engineering, and it is an essential tool for understanding complex systems and making decisions.

In this article, we will look at the fundamentals of time series analysis, such as frequently analyzed data types, techniques and methods used, and some of the primary aspects when working with time series data.

Types of Time Series

There are different types of time series which known as properties that time series characterized by which are:

• Trend which is the time series with downward and upward pattern over time.

• Seasonality is the time series that repeats same pattern after a specific time such as daily, monthly and yearly.

• Cyclic in which patterns in cyclical time series data repeat at irregular intervals.

• Irregularity It is a time series with random fluctuations which is known as randomness with no clear trend and pattern.

• Stationary occurs whenever the time series is not characterized by any trend or seasonal effect. which means it has a constant mean and variance over time.

It is also important to keep in mind that time series data can be a mix of these types; for example, a time series can have both a trend and a seasonal component, or it can be cyclical with a trend component.

Data types used in Time Series

As time series, different types of data can be collected and analyzed. Some examples of data types are:

1. Numerical data: This type of data consist of quantitative measurements.
2. Categorical data: This type of data includes data that can be categorized.
3. Count data: This type of data includes data listed as counts.
4. Temporal data: This type of data includes data collected at a specific period of time.
5. Spatial data: This type of data includes data collected from specific locations.
6. Text data: Data of this type contains unstructured data.

How to prepare and clean data for time series?

Preparing and cleaning data for time series analysis is a critical step in this process as it helps ensure data accuracy. The general steps for preparing and cleaning time series data are:

1. Dealing with non-stationarity: Stationarity is an important assumption in time series analysis. However, many time series datasets are non-stationary. That is, the mean and variance are not constant over time. It is important to manage this non stationarity by using transformation methods such as differencing, log or other methods to stabilize the mean and variance of the data. This is because the presence of non-stationarity will affect the modeling part.
2. Removing missing values: Missing values can occur for a variety of reasons, such as data collection errors or missing data. It is important to either remove or fill in missing values to ensure that the data is complete and accurate. Filling could be done by the mean or median of non-missing values or other methods.
3. Handling of outliers: Outliers are data that are outside the range of most data and can have a significant impact on time series analysis.
4. Feature extraction: Time series data frequently contain information that can be extracted to create additional features. Extracting such features can improve the accuracy of analysis, forecasting, and prediction.

Outlier (Reference: ArcGIS Pro)

Models used for forecasting in time series

There are several different models that can be used for forecasting and prediction in time series analysis:

1. Moving Average: This model calculates the current value of a time series is a linear combination of its past errors. The order of the model (q) represents the number of past errors that are used to predict the current value.
2. Auto Regressive: An AR model can be used to make predictions about future values of a time series based on its past values. The order of the model (p) represents the number of past values that are used to predict the current value.
3. Exponential Smoothing: This model emphasizes the most recent observations; it is useful for data with a trend or seasonality.
4. ARIMA (Auto-Regressive Integrated Moving Average): This model is a combination of the auto-regression (AR) and moving average (MA) models. It’s useful for data that is stationary or can be made stationary. The order of the model are (p,d,q) in which p represents the order of AR, q represents the order of MA and d represents the number of times that the data is differenced to be stationary.
5. SARIMA (Seasonal Auto-Regressive Integrated Moving Average): This model is an extension of the ARIMA model and includes a seasonal component. It’s useful for data that has both a trend and seasonality. The order of this model is SARIMA (p,d,q)(P,D,Q)s ) in which p represents the order of AR, q represents the order of MA and d represents the number of times that the data is differenced to be stationary. And P refers to the number of past seasonal values used, Q refers to the number of past seasonal errors used, D This refers to the order of differencing used to make the data stationary on a seasonal basis, it is used to remove the seasonal component of the data and s is the number of seasons.
6. Prophet: This model is an approach for forecasting time series data based on an additive model that fits annual and weekly seasonality and holiday non-linear trends. Therefore, this model is efficient for forecasting daily data.

How to Analyze Time Series Data?

Exploratory Data Analysis (Reference: devopedia.org)

Analyzing time series data typically involves several steps:

1. Data collection and cleaning:
   This includes ensuring data completeness, accuracy and consistency, and handling missing or inconsistent data.
2. Exploratory Data Analysis (EDA):
   This includes visualizing data to understand patterns and trends that exist in the data. Using time series graphs and representations.
3. Stationarity and seasonality:
   Check if the time series is stationary or nonstationary. If the series is not stationary, it should be stationary by removing any trend and seasonality.
4. Model selection:
   Choose the appropriate time series model or combination of models that best suits your data and analysis objectives.
5. Model fitting:
   Fit the selected model to the data and estimate the model parameters.
6. Model evaluation:
   Evaluate model performance using metrics such as mean absolute error, mean squared error, and mean squared error. Then check if the computed error is normally distributed before predicting it, otherwise you need to process the data further.
7. Forecasting and prediction:
   Use fitted models to predict future values ​​of time series data.

Applications of time series

Time series analysis is a widely used technique in various fields and industries. It has a wide range of practical applications, including weather forecasting, climate forecasting, economic forecasting, healthcare forecasting, engineering forecasting, financial forecasting, retail forecasting, business forecasting, environmental forecasting, social forecasting, and many more.

Weather forecasting (Reference: Analytics Steps)

1. Finance: Time series analysis is used to analyze financial data such as stock prices, interest rates and exchange rates to identify trends and patterns and predict future market movements.
2. Economics: Time series analysis is used to analyze economic data such as unemployment rates in order to understand an economy’s performance over time and forecast future economic conditions.
3. Sales and Marketing: Time series analysis is used in sales and marketing to identify trends and patterns in sales, customer data, and marketing data and to forecast future sales and customer behavior.
4. Healthcare: Time series analysis is used to analyze health data such as medical records, medical device data, and clinical trial data to identify patterns and trends and predict future health conditions.

These are just a few examples, but time series analysis can be applied to many other areas. Specific applications depend on the data set and the purpose of the analysis.

Conclusion

In summary, time series analysis is a powerful approach for understanding and predicting patterns and trends in data over time. It is used in various fields such as finance, business, healthcare, marketing and environmental science. The process of analyzing time series data typically involves multiple steps, including data collection and cleaning, exploratory data analysis, model selection, model fitting, model evaluation, and forecasting.