Your quarterly forecast just missed the mark by 20%, and you're scrambling to understand why. The culprit? You're using time series analysis techniques that worked five years ago but can't keep up with how fast your business moves today.
Most analytics platforms force you to choose between outdated statistical methods that require a PhD to implement or black-box AI that you can't trust. You end up either waiting weeks for your data team to build custom models or getting predictions you can't explain to your stakeholders.
In this guide, we’ll break down the most common time series techniques and show you how to choose the right one based on your data, goals, and constraints.
What is time series analysis (and why now)?
Time series analysis is a statistical method for examining data points collected over time to identify patterns, trends, and relationships. You're essentially looking at historical data to spot recurring behaviors that help predict future outcomes.
For instance, tracking your website traffic daily reveals weekly peaks, seasonal dips, and the immediate impact shown by marketing analytics campaigns.
Why does this matter right now? Market conditions shift overnight, and waiting for weekly reports instead of using real-time analytics puts you at a disadvantage. Time series forecasting gives you the power to recognize patterns as they happen, predict demand with greater accuracy, and catch unusual spikes or drops that need your immediate attention.
Key components that define time series data
Every time series dataset contains four fundamental building blocks. Understanding these components helps you choose the right time series analysis techniques for your specific needs.
Trend: This is the long-term direction your data moves, like quarterly sales figures steadily climbing over three years. You can refine these insights with sales forecasting methods.
Seasonality: Predictable patterns that repeat at fixed intervals, such as ice cream sales spiking every summer or holiday shopping surges
Cyclic patterns: Irregular fluctuations without fixed timing, like economic boom-and-bust cycles affecting your business
Random variations: Unpredictable noise that can't be explained by trends or cycles, such as viral social media posts suddenly driving traffic
3 traditional statistical methods for time series analysis
These proven methods form the foundation of time series analysis and work best when your data shows clear, consistent patterns.
1. Moving averages and exponential smoothing
Moving averages smooth out short-term fluctuations to highlight longer-term trends. A simple moving average calculates the mean of recent data points, while exponential smoothing gives more weight to recent observations.
Here's how this helps you: Apply a 7-day moving average to your daily sales data to see weekly trends without getting distracted by daily noise.
2. ARIMA and seasonal ARIMA models
The Autoregressive Integrated Moving Average (ARIMA) model combines three components for predictions:
Autoregression (AR): Uses past values to predict future ones
Integration (I): Makes data stationary through differencing
Moving Average (MA): Uses past forecast errors to improve accuracy
When your data has seasonal patterns like holiday sales spikes, Seasonal ARIMA (SARIMA) accounts for those repeating cycles.
3. Vector autoregressive models for multiple series
Vector Autoregressive (VAR) models analyze multiple related time series simultaneously. This approach works perfectly when variables influence each other, like how your advertising spend affects website traffic, which then impacts sales over time.
Ready to move beyond manual analysis? See how you can use AI-powered analytics to automate time series forecasting and get answers in seconds. Start your free trial today.
Traditional statistical methods are powerful, but applying them typically requires deep programming knowledge in R or Python. With ThoughtSpot Analytics, you can run complex statistical analysis by asking questions in natural language.
Instead of writing code to implement ARIMA models, you simply type "forecast next quarter's sales based on historical trends" and get instant, interactive results that you can grasp thanks to data literacy insights and act on.
3 modern approaches to time series analysis
When your data contains complex, non-linear patterns that traditional methods miss, machine learning approaches offer more flexibility and power.
1. Neural networks and LSTM for complex patterns
Long Short-Term Memory (LSTM) networks are specialized neural networks designed for sequence data. They excel at learning long-term dependencies that other models overlook, making them ideal for customer churn analysis by analyzing months of subtle interaction patterns.
2. Automated forecasting with Prophet
Developed by Facebook, Prophet is a user-friendly forecasting tool that handles business time series with strong seasonal patterns. It automatically manages holidays, missing data, and trend changes with minimal manual configuration compared to traditional ARIMA models.
3. Ensemble methods and hybrid approaches
Why limit yourself to one model when you can combine several? Ensemble methods blend multiple models to produce more accurate forecasts while reducing the risk of relying on a single model's weaknesses. As Daniel Seymore puts it on an episode of The Data Chief,
"The more algos you create, the better odds you're going to get. As a matter of fact, that's less than 5% of the entire job."
Choosing which ML technique to apply can be overwhelming, but recent AI data trends show how AI-powered platforms simplify this decision. Spotter, your AI Analyst, automatically identifies patterns in your data and suggests appropriate ML techniques.
Instead of spending weeks researching methodologies, you have a conversation with Spotter about your forecasting goals, and it guides you to the most suitable approach while explaining why certain techniques work better for your specific dataset.
How to select the right time series technique: 4 best practices
With multiple options available, follow this framework to make confident decisions about which time series analysis techniques to use.
1. Start with your business objective
Your goal should guide your choice of method:
Forecasting quarterly sales: Consider ARIMA or Prophet for their proven accuracy with business data
Detecting fraudulent transactions: Instant anomaly detection algorithms work better
Understanding marketing campaign impact: VAR models capture the relationships between campaigns and revenue
2. Assess your data characteristics
Let your data inform your decision:
Limited historical data: Stick with simpler methods like moving averages
Clear seasonal patterns: SARIMA or Prophet are strong candidates
Multiple related variables: VAR models or multivariate ML approaches
Complex non-linear relationships: Neural networks or ensemble methods are often best for advanced predictive analytics.
3. Evaluate technical constraints
Be realistic about your resources and requirements:
Computational power: Complex ML models demand significant processing capabilities
Instant needs: Some methods are too slow for live analysis
Your expertise: Match technique complexity to your skills for proper implementation and maintenance.
Interpretability requirements: Statistical models are easier to explain to stakeholders than black-box neural networks
4. Test and validate multiple approaches
Never assume one model is optimal without testing:
Split your data into training and test sets
Compare performance across different methods using consistent metrics
Consider both accuracy and efficiency in your evaluation
Document your selection process for your own future reference and to share with colleagues.
These best practices in action have a serious impact. For example, users at Cox 2M experienced this firsthand. Buried under 1.5 million IoT messages per hour, they had to wait more than five hours for an answer to a single ad-hoc request.
But once they could use ThoughtSpot themselves, the time to get an insight dropped by 88% and annual service costs fell by over $70K.
3 common mistakes in time series technique selection
Even with the right framework, it's easy to fall into these traps that can undermine your analysis.
1. Ignoring stationarity requirements
Many statistical analysis techniques assume your data's properties don't change over time. Applying these models to non-stationary data, like constantly growing revenue streams, produces misleading results.
How to fix it: Always test for stationarity first and apply transformations like differencing or detrending when needed.
2. Overcomplicating with advanced methods
Complex doesn't always mean better. Simple exponential smoothing often outperforms sophisticated neural networks, especially on smaller datasets.
How to fix it: Start with basic methods and only add complexity when justified by significantly better performance.
3. Underestimating computational requirements
Some models are resource-intensive. A deep learning model that works in testing might fail under instant prediction demands.
How to fix it: Consider both training and inference time, and plan for the resources needed as your data volume grows.
How you can implement time series analysis at scale
Choosing the right technique is only half the challenge. You also need to implement it in a way that delivers timely, trusted insights to support your decision-making.
With ThoughtSpot, the difference is significant: instead of waiting for scheduled reports, you get instant access to time series forecasting results that update as new data arrives. You can explore "what if" scenarios, drill into unexpected patterns, and share insights across departments to enable faster data-driven decision making.
Ready to put these time series analysis techniques into practice? Start your free trial today to see how accessible your data becomes when you have AI-powered analytics at your fingertips.
Frequently asked questions about time series analysis techniques
1. How do I analyze multiple related time series datasets together?
Use Vector Autoregressive (VAR) models or multivariate machine learning approaches like LSTMs. These techniques capture the dynamic relationships between different time series, showing how changes in one variable affect others over time.
2. What if I only have a small amount of historical data for analysis?
Start with simple methods like moving averages or basic exponential smoothing when you have limited data. Complex models like neural networks require extensive training data to perform well, so simpler approaches often produce better results with smaller datasets.
3. Can I effectively combine different time series analysis techniques?
Yes, ensemble approaches often deliver more robust forecasts than single models. You can combine a statistical model that captures trends well with a machine learning model that identifies complex patterns, then weight their predictions based on historical performance.
4. How do I explain my chosen technique to non-technical stakeholders?
Focus on business outcomes rather than mathematical details. Instead of explaining ARIMA's autoregressive components, demonstrate how its forecast accuracy translates to better inventory optimization or more reliable revenue projections for your specific use case.
5. What is the minimum amount of historical data needed for reliable forecasting?
This varies by technique, but generally, aim for at least 50-100 data points for basic methods. For seasonal analysis, you need at least 2-3 complete seasonal cycles to capture patterns accurately. Complex machine learning models typically require much more data to avoid overfitting.
