LEADER 04289nam 22007095 450 001 9910416141003321 005 20251215202525.0 010 $a9781484259924 010 $a1484259920 024 7 $a10.1007/978-1-4842-5992-4 035 $a(CKB)4100000011398157 035 $a(MiAaPQ)EBC6318160 035 $a(DE-He213)978-1-4842-5992-4 035 $a(CaSebORM)9781484259924 035 $a(PPN)272268054 035 $a(OCoLC)1204218117 035 $a(OCoLC)on1204218117 035 $a(MiAaPQ)EBC6318106 035 $a(EXLCZ)994100000011398157 100 $a20200824d2020 u| 0 101 0 $aeng 135 $aurcn####||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHands-on time series analysis with Python $efrom basics to bleeding edge techniques /$fby B.V. Vishwas, Ashish Patel 205 $a1st ed. 2020. 210 1$aBerkeley, CA :$cApress,$d[2020]. 215 $a1 online resource (420 pages) 300 $aIncludes index. 311 08$a9781484259917 311 08$a1484259912 327 $aChapter 1: Time Series and its Characteristics -- Chapter 2: Data Wrangling and Preparation for Time Series -- Chapter 3: Smoothing Methods -- Chapter 4: Regression Extension Techniques for Time Series -- Chapter 5: Bleeding Edge Techniques -- Chapter 6: Bleeding Edge Techniques for Univariate Time Series -- Chapter 7: Bleeding Edge Techniques for Multivariate Time Series -- Chapter 8: Prophet. 330 $aLearn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks. You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn: ? Explains basics to advanced concepts of time series ? How to design, develop, train, and validate time-series methodologies ? What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results ? Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. ? Univariate and multivariate problem solving using fbprophet. Who This Book Is For Data scientists, data analysts, financial analysts, and stock market researchers. 606 $aMachine learning 606 $aPython (Computer program language) 606 $aOpen source software 606 $aMachine Learning 606 $aPython 606 $aOpen Source 606 $aAprenentatge automātic$2lemac 606 $aPython (Llenguatge de programaciķ)$2lemac 606 $aSčries temporals$xAnālisi$2lemac 615 0$aMachine learning. 615 0$aPython (Computer program language) 615 0$aOpen source software. 615 14$aMachine Learning. 615 24$aPython. 615 24$aOpen Source. 615 7$aAprenentatge automātic 615 7$aPython (Llenguatge de programaciķ) 615 7$aSčries temporals$xAnālisi 676 $a519.55 700 $aVishwas$b B V$0897556 702 $aPatel$b Ash 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910416141003321 996 $aHands-on Time Series Analysis with Python$92005351 997 $aUNINA