Project Background:
Time Series Data Analysis establishes the context and motivation for delving into the intricate field of time series analysis. Time series data represents a collection of observations recorded sequentially over time, making it crucial in various domains, including finance, healthcare, and industrial manufacturing. The need for robust time series analysis arises from recognizing that this data is abundant and valuable for making informed decisions and predictions. Traditional statistical methods for time series analysis capture complex temporal dependencies, seasonality, and trends in the face of large and diverse datasets. Ultimately, it is a foundation for developing more accurate and data-driven time series analysis methods that facilitate better decision-making and predictive capabilities in numerous applications.
Problem Statement
- The primary problem in time series data analysis is the inherent complexity and uniqueness of time-ordered data. These data are characterized by a sequential collection of observations recorded over time, posing several challenges.
- One major issue is the presence of temporal dependencies, where observations influence the value at one-time step. This intricate temporal autocorrelation makes it difficult to capture and model accurately using traditional statistical methods.
- Time series data often exhibits seasonality, trends to discern and model effectively. Handling missing data and noisy observations can further complicate the analysis.
- In essence, time series data analysis is a developing techniques that can capture the intricate temporal dependencies, patterns, and variations inherent in this data ultimately leading to more accurate predictions, anomaly detection, and actionable insights.
Aim and Objectives
- To develop accurate and adaptable time series analysis methods for improved forecasting, anomaly detection, and trend analysis.
- Enhance predictive accuracy and reliability in time series forecasting.
- Develop robust methods for automated anomaly detection.
- Capture and model intricate temporal dependencies and seasonality.
- Address data preprocessing and handling missing values effectively.
- Accommodate domain-specific needs and variations in data sources.
- Reduce dimensionality and improve efficiency for large-scale time series data.
- Foster the application of time series analysis in diverse fields, from finance to environmental monitoring.
Contributions to Time Series Data Analysis
1. The time series data analysis is multifold and significant. It has improved forecasting accuracy by developing advanced predictive models that effectively capture complex temporal dependencies and patterns in the data.
2. It facilitated automated anomaly detection, helping to identify irregularities and deviations in time-ordered data efficiently. It is especially crucial in fields like manufacturing, where early detection of anomalies can prevent costly breakdowns.
3. Developing effective data preprocessing techniques and handling missing values has streamlined the analysis process, making it more robust and reliable. It is essential when dealing with real-world data that often contains gaps and noise.
4. The adaptability to diverse domains and data sources is a crucial contribution, allowing the application of time series analysis in fields as varied as healthcare, finance, and environmental monitoring.
Deep Learning Algorithms for Time Series Data Analysis
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Convolutional Neural Networks (CNNs)
- Transformer-based Models (BERT, GPT)
- Echo State Networks (ESN)
- Temporal Convolutional Networks (TCN)
- Variational Autoencoders (VAEs)
- Time Series Generative Adversarial Networks (TSGAN)
- Autoregressive Integrated Moving Average (ARIMA)
- Bayesian Recurrent Neural Networks
Datasets for Time Series Data Analysis
- International Airline Passengers dataset
- Environmental Monitoring Data
- Stock Market Price and Volume Data
- Energy Consumption dataset
- Electricity Consumption dataset
- Sunspot Number dataset
- Retail Sales dataset
- Climate Data Time Series
- Exchange Rate dataset
- Bitcoin Price Time Series Data
- Web Traffic Time Series Data
- Sensor Data Time Series
- Financial Market Time Series Data
- Energy Production and Consumption Time Series Data
- COVID-19 Daily Cases and Deaths Time Series Data
Performance Metrics
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
- Mean Absolute Percentage Error (MAPE)
- Mean Absolute Scaled Error (MASE)
- Theils U Statistic
- Forecast Bias
- Forecast Accuracy
- R-squared (R2)
- Explained Variance Score
- Mean Error (ME)
- Mean Percentage Error (MPE)
- Mean Squared Logarithmic Error (MSLE)
- Normalized Root Mean Squared Error (NRMSE)
- Weighted Absolute Percentage Error (WAPE)
- F1-Score for Anomaly Detection
- Precision and Recall for Anomaly Detection
- Area Under the Precision-Recall Curve (AUC-PR)
- Symmetric Mean Absolute Percentage Error (SMAPE)
- Area Under the Receiver Operating Characteristic Curve (AUC-ROC)
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
