LEADER 03726nam 22005655 450 001 9910739454703321 005 20230224133128.0 010 $a9781484289785 010 $a1484289781 024 7 $a10.1007/978-1-4842-8978-5 035 $a(MiAaPQ)EBC7162174 035 $a(Au-PeEL)EBL7162174 035 $a(CKB)25850572300041 035 $a(OCoLC)1356572959 035 $a(OCoLC-P)1356572959 035 $a(DE-He213)978-1-4842-8978-5 035 $a(CaSebORM)9781484289785 035 $a(PPN)268211809 035 $a(EXLCZ)9925850572300041 100 $a20221223d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTime Series Algorithms Recipes $eImplement Machine Learning and Deep Learning Techniques with Python /$fby Akshay R Kulkarni, Adarsha Shivananda, Anoosh Kulkarni, V Adithya Krishnan 205 $a1st ed. 2023. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2023. 215 $a1 online resource (188 pages) 300 $aDescription based upon print version of record. 311 08$aPrint version: Kulkarni, Akshay R. Time Series Algorithms Recipes Berkeley, CA : Apress L. P.,c2023 9781484289778 327 $aChapter 1: Getting Started with Time Series -- Chapter 2: Statistical Univariate Modelling -- Chapter 3: Statistical Multivariate Modelling -- Chapter 4: Machine Learning Regression-Based Forecasting -- Chapter 5: Forecasting Using Deep Learning. 330 $aThis book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. You will: Implement various techniques in time series analysis using Python. Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecasting Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory). 606 $aTime-series analysis$xComputer programs 606 $aTime-series analysis$xData processing 606 $aMachine learning$xComputer programs 606 $aPython (Computer program language) 615 0$aTime-series analysis$xComputer programs. 615 0$aTime-series analysis$xData processing. 615 0$aMachine learning$xComputer programs. 615 0$aPython (Computer program language) 676 $a006.31 702 $aKulkarni$b Akshay R. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910739454703321 996 $aTime Series Algorithms Recipes$93553432 997 $aUNINA