An introduction to time series data with R.pptx
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The document introduces time series data analysis using R, particularly focusing on Malawian agricultural data. It discusses various modeling techniques like ARIMA and Singular Spectrum Analysis (SSA) for predicting future values based on historical data and examines their implications in machine learning and forecasting. The document also emphasizes the dependence of future system behavior on past states and mentions an example dataset related to agricultural land usage in Malawi.
An introduction to time series data with R.pptx
- 1. An introduction to time series data with R and Malawian agricultural data COLLEEN M. FARRELLY, MACHINE LEARNING LEAD
- 2. Who Am I? Consulting machine learning lead, Mpuza Industry researcher in topological data analysis, natural language processing, and time series analytics Co-author of The Shape of Data (No Starch Press)
- 3. Predictions through Time Sales volumes over quarters Pricing of goods or food over weeks Daily stock market trends Yearly disease burden
- 4. Time Dependency Caveat… Future system behavior depends on current and past system states… ◦ Not independent data points ◦ Limits usage of machine learning ◦ Limits accuracy of far-off predictions
- 5. Analyzing Time Series Data: ARIMA Moving averages ◦ Many types of models based on averages over time ◦ Many that add in an autoregressive piece to account for correlations across time periods ◦ Prediction based on autoregression and moving average
- 6. Analyzing Time Series Data: SSA Another approach is to decompose the time series (singular spectrum analysis): ◦ Embed the time series ◦ Perform spectral decomposition (singular value decomposition…) ◦ Group eigentriples and average across the matrix diagonal ◦ Linear prediction of future time periods
- 7. More Advanced Methods Time-lag components in machine learning models (deep learning, KNN, random forest…) Partial differential equation models (SIR models for epidemics…) Forecasting future values Detecting changes in system behavior
- 8. Example Dataset: ARIMA and SSA 1961-2013 agricultural land usage in Malawi Cleaned up from World Bank’s data on climate change indicators by country (Humanitarian Data Exchange)