Advanced Time-Series: Types, Methods, Applications and Top 20 Python Libraries 📈
Advanced time series forecasting involves using machine learning, and deep learning techniques to predict future values of time-dependent data, accounting for complex patterns and seasonality, trends.
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📈 Time series Types:
✶ Univariate
✶ Multivariate
✶ Stationary
✶ Non-Stationary
✶ Seasonal
✶ Non-Seasonal
✶ Irregular
✶ Regular
✶ Additive
✶ Multiplicative
✶ Periodic
✶ Non-Periodic
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⌛️ Here are several advanced time series forecasting methods:
› LSTM (Long Short-Term Memory) Networks: A type of recurrent neural network (RNN) capable of learning long-term dependencies.
› GRU (Gated Recurrent Unit) Networks: Similar to LSTM but with a simpler architecture.
› Transformer Models: Uses attention mechanisms to capture dependencies without relying on sequential data.
› TBATS (Trigonometric, Box-Cox, ARMA, Trend, Seasonal): Handles multiple seasonalities and complex seasonal patterns.
› XGBoost (Extreme Gradient Boosting): An implementation of gradient-boosted decision trees designed for speed and performance.
› N-BEATS (Neural Basis Expansion Analysis): A neural network-based approach designed specifically for time series forecasting.
› TFT (Temporal Fusion Transformers): Combines the interpretability of transformers with temporal fusion for time series forecasting.
› Large Language Models (LLMs): LLMs like GPT-4 can be adapted for time series forecasting by encoding time series as text, using embeddings, fine-tuning pre-trained models, combining with traditional methods, and leveraging contextual understanding from text-based data.
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📊 Applications:
♕ Predictive Maintenance
♕ Healthcare Monitoring and Forecasting
♕ Energy Consumption Forecasting
♕ Supply Chain Optimization
♕ Natural Language Processing for Temporal Data
♕ Sensor Data Analysis
♕ Traffic Flow Prediction
♕ Sales and Revenue Forecasting
♕ Economic Indicators Forecasting
♕ Climate Modeling
♕ Stock Price Prediction
♕ Cryptocurrency Price Prediction
♕ Customer Churn Prediction
♕ Social Media Trend Analysis
♕ Fraud Detection
♕ Real-time Event Detection and Response
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I found the following 20 Libraries on Time-Series based on GitHub stars:
📚Sktime
📚Darts
📚tsfresh
📚NeuralProphet
📚STUMPY
📚pmdarima
📚tslearn
📚GluonTS
📚Pytorch-forecasting
📚StatsForecast
📚Streamz
📚Uber/orbit
📚pyts
📚NeuralForecast
📚greykite
📚TSFEL
📚seglearn
📚tick
📚Auto_TS
📚DeepAR
Do you know other Time-series libraries or functions?
🔗Source:
https://github.com/PacktPublishing/Modern-Time-Series-Forecasting-with-Python
https://www.datasciencewithmarco.com/blog/timesnet-the-latest-advance-in-time-series-forecasting
https://machinelearningmastery.com/time-series-prediction-with-deep-learning-in-python-with-keras/
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