MM - Wirte short note on 1) Time series Analysis il) TF- IDF.

Creating a mind map involves identifying key concepts and their relationships to help with future recall. Here are the keywords and short sentences for both Time Series Analysis and TF-IDF:

Time Series Analysis

Main Concept: Time Series Analysis

Branches and Keywords:

1. Components of Time Series:

   • Trend: Long-term direction.
   • Seasonal: Regular periodic fluctuations.
   • Cyclical: Irregular, non-periodic fluctuations.
   • Irregular: Random, unpredictable variations.

2. Methods and Models:

   • Decomposition: Splitting into trend, seasonal, irregular.
   • Smoothing Techniques: Moving averages, exponential smoothing.
   • ARIMA: Combines AR, MA, and integration.
   • SARIMA: ARIMA with seasonal differencing.
   • ETS Model: Error, trend, and seasonal components.

3. Applications:

   • Finance: Stock prediction.
   • Economics: GDP forecasting.
   • Weather: Temperature prediction.
   • Sales: Demand forecasting.

TF-IDF

Main Concept: TF-IDF

Branches and Keywords:

1. Components:

   • Term Frequency (TF): Frequency of term in document.
   • Inverse Document Frequency (IDF): Importance across corpus.
   • TF-IDF Calculation: TF multiplied by IDF.

2. Formulas:

   • TF: (Term occurrences in document) / (Total terms in document).
   • IDF: log(Total documents / Documents with term).
   • TF-IDF: TF * IDF.

3. Applications:

   • Text Mining: Extract significant words.
   • Information Retrieval: Enhance search accuracy.
   • NLP: Feature extraction for machine learning.
   • Content Recommendation: Recommend based on term importance.

Mind Map Structure

1. Time Series Analysis:

   • Components of Time Series:
     • Trend: Long-term direction.
     • Seasonal: Regular fluctuations.
     • Cyclical: Irregular patterns.
     • Irregular: Random variations.
   • Methods and Models:
     • Decomposition: Splitting components.
     • Smoothing: Moving averages, exponential.
     • ARIMA: AR + MA + integration.
     • SARIMA: ARIMA with seasonality.
     • ETS: Error, trend, seasonal.
   • Applications:
     • Finance: Stock prices.
     • Economics: GDP trends.
     • Weather: Forecasting temperatures.
     • Sales: Demand planning.

2. TF-IDF:

   • Components:
     • TF: Term frequency in document.
     • IDF: Importance in corpus.
     • TF-IDF: Product of TF and IDF.
   • Formulas:
     • TF: Term occurrences / Total terms.
     • IDF: log(Total docs / Docs with term).
     • TF-IDF: TF * IDF.
   • Applications:
     • Text Mining: Extract key terms.
     • Info Retrieval: Improve searches.
     • NLP: Feature extraction.
     • Content Recommendation: Based on term importance.

This structure can help create a visual mind map with the main concepts, their sub-components, and practical applications for quick and effective recall.