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I wanted to explore the claim of "Time Series Made Easy in Python" by the Darts library. Turns out it takes ~12 lines of code including imports to get started with Darts.
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Adding interactive web elements with Streamlit to the Darts documentation example led to this quick demo project that lets you explore any univariate Timeseries CSV and make forecasts with Exponential Smoothing. This version will resample and sum values to get to monthly samples (or change to weekly / quarterly / etc); there are other Pandas resampling aggregation options though!
Related posts
- Darts: Python lib for forecasting and anomaly detection on time series
- [D] Doubts on the implementation of LSTMs for timeseries prediction (like including weather forecasts)
- [D] Hybrid forecasting framework ARIMA-LSTM
- [D] Do any of you have experience using Darts for forecasting?
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gluonts VS darts - a user suggested alternative
2 projects | 13 Apr 2023